DETAILED ACTION
Remarks
Claims 1, 2, 4-10, 12-22 have been examined and rejected. This Office action is responsive to the amendment filed on 08/13/2026, which has been entered in the above identified application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 4-10, and 12-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, claim 1 recites “a plurality of icons each indicating performance of the chamber in association with one of the first plurality of subsystems and one of the second plurality of groups of data”. It is unclear whether “the chamber” is intended to refer to the plurality of process chambers or the first chamber. It is unclear whether “in association with one of the first plurality of subsystems” is intended to modify the plurality of icons, the performance, or the chamber. It is unclear whether “and one of the second plurality of groups of data” is intended to modify the plurality of icons, the performance, the chamber, or the association. For the purpose of examination, this limitation is interpreted as: a plurality of icons, wherein each icon of the plurality of icons indicates a respective performance of a respective chamber, wherein the respective performance of the respective chamber is based on one of the first plurality of groups of data and one of the second plurality of groups of data
Regarding claims 9 and 17, claims 9 and 17 contain substantially similar limitations to those found in claim 1. Consequently, claims 9 and 17 are rejected for the same reasons.
Regarding claims 2, 4-8, 10, 12-16, and 18-22, claims 2, 4-8, 10, 12-16, and 18-22 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending on an indefinite parent claim.
Regarding claims 2 and 10, claims 2 and 10 recite “the indicator of the summary of performance of the first chamber of the plurality of process chambers”. It is unclear how this limitation is intended to relate to the previously recited indicator of a summary of performance. For the purposes of examination, this limitation is interpreted as: a first indicator of the summary of performance of the first chamber of the plurality of process chambers
Claims 2 and 10 further recite “a visual icon displayed as part of the first UI element in connection with the first chamber”. It is unclear which previous limitation “in connection with the first chamber” is intended to modify. For the purposes of examination, this limitation is interpreted as: a visual icon displayed as a part of the first UI element, wherein the visual icon is displayed in connection with the first chamber
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-6, 9, 10, 12-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni et al. (US 20100249976 A1, published 09/30/2010), hereinafter Aharoni in view of Kuyel (US 20180269067 A1, published 09/20/2018).
Regarding claim 17, Aharoni teaches the claim comprising:
A system, comprising memory and one or more processors coupled to the memory, wherein the one or more processors are configured to (Aharoni Figs. 1-21; [0089], FIG. 3 illustrates a part of an exemplary detailed report including graphs 320-370 invoked, e.g., by clicking 310 a cell 300 in the chamber-recipe report 290. Upon selecting or clicking a cell in the chamber-recipe report 290, a computing device (e.g., a computing device 1600 in FIG. 18) including a CPU (e.g., a CPU 1611 in FIG. 18) and memory (e.g., a RAM 1614 in FIG. 18) executes code in the memory to generate graphs 320-370; [0132], FIG. 18 illustrates a hardware configuration of a computing system 1600 generating the chamber-recipe report, the engineer's report, the manager's report and a detailed report after executing the above-mentioned statistical tests. The hardware configuration preferably has at least one processor or central processing unit (CPU)):
provide, via a graphical user interface (GUI), a first user interface (UI) element, the first UI element comprising an indicator of a summary of performance, wherein the summary of performance is based on performance data of a plurality of process chambers; obtain a user selection, via the first UI element, of a first chamber of the plurality of process chambers (Aharoni Figs. 1-21; [0060], a "heat map" is a special type of color based data visualization format that is well suited to enable analyzing large data volumes using an intuitive graphical display; [0061], heat maps display data using different colors or shade gradations. Heat maps are helpful for spotting trends and making a quick determination of high, low and changes of value statistics. Heat maps can be applied to a single column (e.g., by illustrating a heat map associated with a single recipe step), or to all columns (e.g., by illustrating a heat map associated with a recipe); [0076], Each row of a heat map corresponds to a sensor. Each column of a heat map corresponds to a recipe step. There may be at least one sensor per a tool or a wafer to obtain trace data of a recipe step while manufacturing at least one microelectronic device (e.g., a wafer or a semiconductor chip). Then, the computing device 1600 analyzes the obtained trace data to determine a level of operational significance found in the obtained trace data, e.g., by executing the above-mentioned statistical tests. For example, the computing device 1600 executes the statistical tests, and finds a maximum value of the statistical tests. As the maximum value of the statistical tests is lower, a corresponding trace data has higher operational interest. In other words, a low score value (e.g., 0.1) indicate an aberrant condition of a corresponding tool. The level of the operational interest refers to a degree of influence affecting a wafer quality or tool operation, e.g., how stable a tool is or how well matched a tool is. The computing device 1600 generates a score, e.g., results from a single statistical test or maximum, minimum, average or other combinations of the statistical tests, which indicates the level of the operational significance, and assigns the generated score to each cell. The computing device 1600 places the score in a corresponding cell of a heat map. Engineers or managers use scores in the cell to evaluate a semiconductor manufacturing tool performance; [0077], The engineer or managers may use the colored cells in the heat map to evaluate the tool performance or behavior; [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report. The chamber-recipe report 290 comprises recipe steps 460 at columns and sensor data or parameters 480 at rows. The engineer's report 430 is a middle-level report. A cell in the engineer's report 430 corresponds to a chamber-recipe report. Thus, clicking a cell in the engineer's report 430 brings up a corresponding chamber-recipe report. The engineer's report 430 includes recipes 440 at columns and machine tool devices, e.g., chambers 470, at rows. The manager's report 400 is the highest-level report. A cell in the manager's report corresponds to an engineer's report. Thus, clicking a cell in the manager's report brings up a corresponding engineer's report. The manager's report 400 includes recipe groups 410 at columns and chamber groups 420 at rows. In the manager's report, a group of recipes and a group of chambers belongs to an engineer responsible for the corresponding tool/process; [0093], FIG. 5 illustrates a detailed view of an exemplary engineer's report generated in accordance with the invention. The engineer's report is a summary of a small (e.g., 1) or large number (e.g., on the order of 10.sup.6) of heat maps. Each section 500, e.g., a section labelled "Tool_2" in the exemplary engineer's report in FIG. 5, represents a chamber in a semiconductor manufacturing tool. In the engineer's report, rows such as rows 510 per a section represent interesting rows (i.e., interesting sensors) in chamber-recipe reports. Thus, the engineer's report assists an engineer in knowing which chambers and recipes warrant attention. Many individual chamber recipe reports may be generated for one engineer);
and responsive to the user selection, provide a second UI element comprising visual indications of a performance of the first chamber, the second UI element comprising: a first axis, corresponding to a first division into a first plurality of components of the first chamber; a second axis, corresponding to a second division into a second plurality of groups of data associated with the first chamber; and a plurality of icons each indicating performance of the chamber in association with one of the first plurality of components and one of the second plurality of groups of data (Aharoni Figs. 1-21; [0075], FIG. 2 illustrates a view of an exemplary chamber-recipe report generated by the computing device 1600, e.g., by executing the above-mentioned statistical tests, in according to one embodiment of the present invention. The chamber-recipe report 290 includes, but is not limited to, three heat maps. A first heat map 230 represents past or reference data obtained from sensors. A second heat map 210 represents current data obtained from sensors. A third heat map 200 represents difference between analyses of past data and current data. Rows 270 in the chamber-recipe report 290 correspond to sensors, e.g., an argon gas flow sensor 280, or other parameters (e.g., temperatures, pressures, etc.) measured from sensors. Columns 240, 250 and 260 correspond to recipe steps which are semiconductor manufacturing steps within a process or steps within a recipe to fabricate microelectronic devices. Cells are intersections of the rows 270 and the columns 240, 250 and 260; [0076], Each row of a heat map corresponds to a sensor. Each column of a heat map corresponds to a recipe step. There may be at least one sensor per a tool or a wafer to obtain trace data of a recipe step while manufacturing at least one microelectronic device (e.g., a wafer or a semiconductor chip); [0078], Thus, engineers or managers can identify recipe steps, sensors, or time periods when tools are unstable and/or mismatched, e.g., through the reports such as a chamber-recipe report 290 shown in FIG. 2. The chamber-recipe report 290 describes trace data and their analyses in multiple contexts: 1. The behavior of a sensor across steps in a process; 2. The behavior of other sensors on the same tool; 3. The behavior of one tool or sensor as a member of a set of nominally identical tools or sensors; and 4. The behavior of processes, sensors, or tools in the context of current and reference time periods. The chamber-recipe report 290 includes links to detailed reports such as graphs 320-370 in FIG. 3, e.g., clicking a cell in the report 290 brings up at least four graphs representing the four different views of data (e.g., different view of analyses of process trace data). The scores may be understood by looking at graphs, but the exemplary graphs 320-370 illustrated in FIG. 3 are independent of the particular number or kind of statistical tests done on the data or summary statistics extracted there from. However, graphs (not shown) in the detailed reports can illustrate data or summary statistics from the statistical tests. The chamber-recipe report 290 may summarize multiple statistical test results from a single analysis, e.g., by assigning a single number (score) which is a maximum, minimum, average, other value of the multiple analyses to each cell as above. The chamber-recipe report 290 employs the above-mentioned statistical tests to analyze the trace data from sensors. The chamber-recipe report 290 provides analyses of trace data in isolation as well as predictors of tool performance. The chamber-recipe report 290, implemented as part of a Tool Stability application 106 or Tool Matching application 108, may report results of analyses of trace data in isolation. The chamber-recipe report 290, implemented as part of a Tool Insight application 110 may report analyses of trace data as predictors of performance on wafers. According to one embodiment, a tool behavior refers to a tool operation and trace data of the tool. Performance of a tool refers to test results on products (e.g., wafers) and measurements (e.g., yield rates) made on the products; [0089], FIG. 3 illustrates a part of an exemplary detailed report including graphs 320-370 invoked, e.g., by clicking 310 a cell 300 in the chamber-recipe report 290; [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report. The chamber-recipe report 290 comprises recipe steps 460 at columns and sensor data or parameters 480 at rows. The engineer's report 430 is a middle-level report. A cell in the engineer's report 430 corresponds to a chamber-recipe report. Thus, clicking a cell in the engineer's report 430 brings up a corresponding chamber-recipe report. The engineer's report 430 includes recipes 440 at columns and machine tool devices, e.g., chambers 470, at rows. The manager's report 400 is the highest-level report. A cell in the manager's report corresponds to an engineer's report. Thus, clicking a cell in the manager's report brings up a corresponding engineer's report. The manager's report 400 includes recipe groups 410 at columns and chamber groups 420 at rows. In the manager's report, a group of recipes and a group of chambers belongs to an engineer responsible for the corresponding tool/process; see also [0077], [0093])
However, Aharoni fails to expressly disclose a first axis, corresponding to a first division into a first plurality of subsystems of the first chamber, wherein a subsystem comprises a plurality of related components associated with a function of the first chamber, and wherein the first plurality of subsystems comprises at least one of a temperature control subsystem, a gas delivery subsystem, a RF delivery subsystem, or a pressure subsystem; a plurality of icons each indicating performance of the chamber in association with one of the first plurality of subsystems. In the same field of endeavor, Kuyel teaches:
a first axis, corresponding to a first division into a first plurality of subsystems of the first chamber, wherein a subsystem comprises a plurality of related components associated with a function of the first chamber, and wherein the first plurality of subsystems comprises at least one of a temperature control subsystem, a gas delivery subsystem, a RF delivery subsystem, or a pressure subsystem; a plurality of icons each indicating performance of the chamber in association with one of the first plurality of subsystems (Kuyel Figs. 1-15; [0028], PEALD is carried out inside a chamber, at the one end of which a substrate/wafer is placed above a platen which is heated by a platen heater. Preferably, the platen heater heats the platen and the substrate by resistive heating. The platen heater thus heats the substrate to a desired temperature; [0221], FIG. 13 shows a screenshot from the control software introduced above that is used to control/manipulate the instant ALD system design for executing a recipe. Specifically, screenshot 350 of FIG. 13 shows a pressure chart 352A indicating pressure versus time. The pressure readout can be selected from toggle button 352B to either general system pressure measured by a wide range gauge (WRG), or ALD volume or process chamber pressure measured by a Pirani gauge. There are several choices of instruments available from vendors for these gauges to the skilled artisans; [0233], In the lower region of the screen indicated by reference numeral 360 are status bars of various components and sub-systems, including cooling subsystem, compressed dry air (CDA), venting status of the chamber, RF status, etc; [0234], Further shown are MFC controls 362 showing which gases are being flowed at what pressure, RF controls 364 and pressure controls 366. Also shown are various temperature setpoints and corresponding readings 368. These include the temperature setpoints and current temperature readings for platen sample heater 142 (see FIG. 4 and FIG. 6), chamber 100, turbo pump 192, reservoirs/cylinders 202, 204, 206 (see FIG. 8) and 214A-B and 216 (see FIG. 9), and valves/lines, etc. as shown; [0235], For completeness, FIG. 14 shows screenshot 380 of the control software of the instant design from an actual recipe depositing a GaN film using GaCl3 as the precursor)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated a first axis, corresponding to a first division into a first plurality of subsystems of the first chamber, wherein a subsystem comprises a plurality of related components associated with a function of the first chamber, and wherein the first plurality of subsystems comprises at least one of a temperature control subsystem, a gas delivery subsystem, a RF delivery subsystem, or a pressure subsystem; a plurality of icons each indicating performance of the chamber in association with one of the first plurality of subsystems as suggested in Kavaklioglu into Aharoni. Doing so would be desirable because it is believed that widespread adoption of manufacturing technology for a variety of promising industrial applications is predicated upon obtaining uniformity. It is also important to reduce the cycle-time so that operational throughput can be increased (see Kuyel [0019]). The prior art cited above fails to accomplish these goals (see Kuyel [0020]). The prior art is unable to satisfy the very high quality and uniformity, low cost and high throughput requirements of many industrial applications (see Kuyel [0021]). Additionally, the system of Kuyel would improve the system of Aharoni by providing additional desired detail in the reports, such as subsystems in addition to the individual sensors and tools, thereby making the reports easier to read and better enabling the user to understand and diagnose undesirable results.
Regarding claims 1 and 9, claims 1 and 9 contain substantially similar limitations to those found in claim 17. Consequently, claims 1 and 9 are rejected for the same reasons.
Regarding claim 2, Aharoni in view of Kuyel teaches all the limitations of claim 1, further comprising:
wherein the indicator of the summary of performance of the first chamber of the plurality of process chambers comprises a visual icon displayed as part of the first UI element in connection with the first chamber, wherein the visual icon comprises a color or pattern to indicate a quality of the performance of the first chamber, and wherein the summary of performance is based on performance data of the performance data of the plurality of process chambers (Aharoni Figs. 1-21; [0076], Each row of a heat map corresponds to a sensor. Each column of a heat map corresponds to a recipe step. There may be at least one sensor per a tool or a wafer to obtain trace data of a recipe step while manufacturing at least one microelectronic device (e.g., a wafer or a semiconductor chip). Then, the computing device 1600 analyzes the obtained trace data to determine a level of operational significance found in the obtained trace data, e.g., by executing the above-mentioned statistical tests. For example, the computing device 1600 executes the statistical tests, and finds a maximum value of the statistical tests. As the maximum value of the statistical tests is lower, a corresponding trace data has higher operational interest. In other words, a low score value (e.g., 0.1) indicate an aberrant condition of a corresponding tool. The level of the operational interest refers to a degree of influence affecting a wafer quality or tool operation, e.g., how stable a tool is or how well matched a tool is. The computing device 1600 generates a score, e.g., results from a single statistical test or maximum, minimum, average or other combinations of the statistical tests, which indicates the level of the operational significance, and assigns the generated score to each cell. The computing device 1600 places the score in a corresponding cell of a heat map. Engineers or managers use scores in the cell to evaluate a semiconductor manufacturing tool performance; [0077], The engineer or managers may use the colored cells in the heat map to evaluate the tool performance or behavior; [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report. The chamber-recipe report 290 comprises recipe steps 460 at columns and sensor data or parameters 480 at rows. The engineer's report 430 is a middle-level report. A cell in the engineer's report 430 corresponds to a chamber-recipe report. Thus, clicking a cell in the engineer's report 430 brings up a corresponding chamber-recipe report. The engineer's report 430 includes recipes 440 at columns and machine tool devices, e.g., chambers 470, at rows. The manager's report 400 is the highest-level report. A cell in the manager's report corresponds to an engineer's report. Thus, clicking a cell in the manager's report brings up a corresponding engineer's report. The manager's report 400 includes recipe groups 410 at columns and chamber groups 420 at rows. In the manager's report, a group of recipes and a group of chambers belongs to an engineer responsible for the corresponding tool/process; [0093], FIG. 5 illustrates a detailed view of an exemplary engineer's report generated in accordance with the invention. The engineer's report is a summary of a small (e.g., 1) or large number (e.g., on the order of 10.sup.6) of heat maps. Each section 500, e.g., a section labelled "Tool_2" in the exemplary engineer's report in FIG. 5, represents a chamber in a semiconductor manufacturing tool. In the engineer's report, rows such as rows 510 per a section represent interesting rows (i.e., interesting sensors) in chamber-recipe reports. Thus, the engineer's report assists an engineer in knowing which chambers and recipes warrant attention. Many individual chamber recipe reports may be generated for one engineer)
Regarding claim 10, claim 10 contains substantially similar limitations to those found in claim 2. Consequently, claim 10 is rejected for the same reasons.
Regarding claim 4, Aharoni in view of Kuyel teaches all the limitations of claim 1, further comprising:
wherein the second division of data comprises a division into operations of a substrate processing procedure performed in the first chamber (Aharoni Figs. 1-21; [0075], FIG. 2 illustrates a view of an exemplary chamber-recipe report generated by the computing device 1600, e.g., by executing the above-mentioned statistical tests, in according to one embodiment of the present invention. The chamber-recipe report 290 includes, but is not limited to, three heat maps. A first heat map 230 represents past or reference data obtained from sensors. A second heat map 210 represents current data obtained from sensors. A third heat map 200 represents difference between analyses of past data and current data. Rows 270 in the chamber-recipe report 290 correspond to sensors, e.g., an argon gas flow sensor 280, or other parameters (e.g., temperatures, pressures, etc.) measured from sensors. Columns 240, 250 and 260 correspond to recipe steps which are semiconductor manufacturing steps within a process or steps within a recipe to fabricate microelectronic devices. Cells are intersections of the rows 270 and the columns 240, 250 and 260; [0076], Each row of a heat map corresponds to a sensor. Each column of a heat map corresponds to a recipe step. There may be at least one sensor per a tool or a wafer to obtain trace data of a recipe step while manufacturing at least one microelectronic device (e.g., a wafer or a semiconductor chip); [0078], Thus, engineers or managers can identify recipe steps, sensors, or time periods when tools are unstable and/or mismatched, e.g., through the reports such as a chamber-recipe report 290 shown in FIG. 2. The chamber-recipe report 290 describes trace data and their analyses in multiple contexts: 1. The behavior of a sensor across steps in a process; 2. The behavior of other sensors on the same tool; 3. The behavior of one tool or sensor as a member of a set of nominally identical tools or sensors; and 4. The behavior of processes, sensors, or tools in the context of current and reference time periods. The chamber-recipe report 290 includes links to detailed reports such as graphs 320-370 in FIG. 3, e.g., clicking a cell in the report 290 brings up at least four graphs representing the four different views of data (e.g., different view of analyses of process trace data). The scores may be understood by looking at graphs, but the exemplary graphs 320-370 illustrated in FIG. 3 are independent of the particular number or kind of statistical tests done on the data or summary statistics extracted there from. However, graphs (not shown) in the detailed reports can illustrate data or summary statistics from the statistical tests. The chamber-recipe report 290 may summarize multiple statistical test results from a single analysis, e.g., by assigning a single number (score) which is a maximum, minimum, average, other value of the multiple analyses to each cell as above. The chamber-recipe report 290 employs the above-mentioned statistical tests to analyze the trace data from sensors. The chamber-recipe report 290 provides analyses of trace data in isolation as well as predictors of tool performance. The chamber-recipe report 290, implemented as part of a Tool Stability application 106 or Tool Matching application 108, may report results of analyses of trace data in isolation. The chamber-recipe report 290, implemented as part of a Tool Insight application 110 may report analyses of trace data as predictors of performance on wafers. According to one embodiment, a tool behavior refers to a tool operation and trace data of the tool. Performance of a tool refers to test results on products (e.g., wafers) and measurements (e.g., yield rates) made on the products; [0089], FIG. 3 illustrates a part of an exemplary detailed report including graphs 320-370 invoked, e.g., by clicking 310 a cell 300 in the chamber-recipe report 290; [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report. The chamber-recipe report 290 comprises recipe steps 460 at columns and sensor data or parameters 480 at rows. The engineer's report 430 is a middle-level report. A cell in the engineer's report 430 corresponds to a chamber-recipe report. Thus, clicking a cell in the engineer's report 430 brings up a corresponding chamber-recipe report. The engineer's report 430 includes recipes 440 at columns and machine tool devices, e.g., chambers 470, at rows. The manager's report 400 is the highest-level report. A cell in the manager's report corresponds to an engineer's report. Thus, clicking a cell in the manager's report brings up a corresponding engineer's report. The manager's report 400 includes recipe groups 410 at columns and chamber groups 420 at rows. In the manager's report, a group of recipes and a group of chambers belongs to an engineer responsible for the corresponding tool/process; see also [0077], [0093])
Regarding claims 12 and 18, claims 12 and 18 contain substantially similar limitations to those found in claim 4. Consequently, claims 12 and 18 are rejected for the same reasons.
Regarding claim 5, Aharoni in view of Kuyel teaches all the limitations of claim 1, further comprising:
further comprising performing a corrective action based on the performance data of the plurality of process chambers, the corrective action comprising one or more of: updating a process recipe; scheduling corrective maintenance; or scheduling preventative maintenance (Aharoni Figs. 1-21; [0075-0076], Each row of a heat map corresponds to a sensor. Each column of a heat map corresponds to a recipe step. There may be at least one sensor per a tool or a wafer to obtain trace data of a recipe step while manufacturing at least one microelectronic device (e.g., a wafer or a semiconductor chip). Then, the computing device 1600 analyzes the obtained trace data to determine a level of operational significance found in the obtained trace data, e.g., by executing the above-mentioned statistical tests. For example, the computing device 1600 executes the statistical tests, and finds a maximum value of the statistical tests. As the maximum value of the statistical tests is lower, a corresponding trace data has higher operational interest. In other words, a low score value (e.g., 0.1) indicate an aberrant condition of a corresponding tool; [0078], Thus, engineers or managers can identify recipe steps, sensors, or time periods when tools are unstable and/or mismatched, e.g., through the reports such as a chamber-recipe report 290 shown in FIG. 2; [0089], FIG. 3 illustrates a part of an exemplary detailed report including graphs 320-370 invoked, e.g., by clicking 310 a cell 300 in the chamber-recipe report 290; [0092-0093], the engineer's report assists an engineer in knowing which chambers and recipes warrant attention. Many individual chamber recipe reports may be generated for one engineer; [0109], There may be provided a tool box for reviewing the faults and maintenance history of the tool. After reviewing the faults and maintenance history, at step 735, the user evaluates whether an immediate action is required. If the immediate action is required, at step 755, the user stops a corresponding tool, notifies a maintenance team to fix the tool; [0110], at step 765, the user may initiate an action to add a maintenance task; The computing device 1600 may store log or action history of the user and the maintenance schedule; [0119], At step 2400, a user identifies an unstable or mismatched chamber in a tool while reviewing a report such as a chamber-recipe report 290. The user may be able to locate the report, e.g., via an entry point (i.e., a component which provides navigations to diverse reports such as chamber-recipe reports and/or engineer's reports). At step 2405, the user evaluates the identified chamber, e.g., via a tool box which provides the user with a description of FDC faults or other descriptors of the status of the chamber or a maintenance activity of the chamber. Information about whether the identified chamber is down is gathered; [0121], At step 2430, the user determines whether the abnormal behaviour detected in the tool will lead to degraded or scrapped product. If the user determines that the abnormal behaviour will lead to degraded or scrapped product, at step 2440, the user stops a tool having the chamber, notifies that the tool is temporarily out of service, and contacts a maintenance team to fix the tool. The user may use existing applications such as emails or instant messages for the notifications. After executing the step 2440, the control proceeds to step 2460; [0122], the user contacts the maintenance team to adjust the change in the tool, e.g., by sending an email or an instant message. Otherwise, at step 2460, the user initiates the computing device 1600 to record a log history of the chamber and the maintenance team activity; [0127], the user schedules a maintenance task with a maintenance team; see also [0077])
Regarding claims 13 and 19, claims 13 and 19 contain substantially similar limitations to those found in claim 5. Consequently, claims 13 and 19 are rejected for the same reasons.
Regarding claim 6, Aharoni in view of Kuyel teaches all the limitations of claim 1, further comprising:
further comprising obtaining, by the processing device, a user selection of the plurality of process chambers from a set of pluralities of process chambers, wherein providing the first UI element is performed responsive to the user selection of the plurality of process chambers (Aharoni Figs. 1-21; [0068], Each manager's report includes a set of three heat maps, one heat map reporting analyses of recent data, one heat map reporting analyses of data from a past or reference period, and one heat map reporting differences in analyses between the two time periods. Individual cells in each heat map report summary analyses for individual recipe groups and chamber groups. Each cell is linked to a corresponding engineer's report; [0076-0077], The engineer or managers may use the colored cells in the heat map to evaluate the tool performance or behavior; [0078], The chamber-recipe report 290 includes links to detailed reports such as graphs 320-370 in FIG. 3, e.g., clicking a cell in the report 290 brings up at least four graphs representing the four different views of data (e.g., different view of analyses of process trace data); [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report. The chamber-recipe report 290 comprises recipe steps 460 at columns and sensor data or parameters 480 at rows. The engineer's report 430 is a middle-level report. A cell in the engineer's report 430 corresponds to a chamber-recipe report. Thus, clicking a cell in the engineer's report 430 brings up a corresponding chamber-recipe report. The engineer's report 430 includes recipes 440 at columns and machine tool devices, e.g., chambers 470, at rows. The manager's report 400 is the highest-level report. A cell in the manager's report corresponds to an engineer's report. Thus, clicking a cell in the manager's report brings up a corresponding engineer's report. The manager's report 400 includes recipe groups 410 at columns and chamber groups 420 at rows. In the manager's report, a group of recipes and a group of chambers belongs to an engineer responsible for the corresponding tool/process; [0093], FIG. 5 illustrates a detailed view of an exemplary engineer's report generated in accordance with the invention. The engineer's report is a summary of a small (e.g., 1) or large number (e.g., on the order of 10.sup.6) of heat maps. Each section 500, e.g., a section labelled "Tool_2" in the exemplary engineer's report in FIG. 5, represents a chamber in a semiconductor manufacturing tool. In the engineer's report, rows such as rows 510 per a section represent interesting rows (i.e., interesting sensors) in chamber-recipe reports. Thus, the engineer's report assists an engineer in knowing which chambers and recipes warrant attention. Many individual chamber recipe reports may be generated for one engineer; [0094], FIG. 6 illustrates a section of an exemplary engineer's report on the left and portions of four exemplary detailed reports on the right. For example, clicking a first cell 600 in the engineer's report invokes generating a detailed report which includes a graph 650. Clicking a second cell 610 in the engineer's report invokes generating a graph 660. Clicking a third cell 620 in the engineer's report invokes generating a graph 670. Clicking a fourth cell 640 in the engineer's report invokes generating a graph 680)
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 6. Consequently, claim 14 is rejected for the same reasons.
Claims 7, 8, 15, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of Kuyel in further view of Kavaklioglu et al. (US 20070067142 A1, published 03/22/2007), hereinafter Kavaklioglu, in further view of Huang et al. (US 20080244400 A1, published 10/02/2008), hereinafter Huang.
Regarding claim 7, Aharoni in view of Kuyel teaches all the limitations of claim 1, further comprising:
wherein the summary of performance is based on a combination that is based on violations of known methods, wherein the combination is indicated by the performance data of the plurality of process chambers (Aharoni Figs. 1-21; [0072], the present invention analyzes each sensor individually, provides linked heat maps (e.g., clicking a cell in a heat map invokes a lower-level report (i.e., detailed report)), aggregates multiple analyses (e.g., by averaging scores in a lower-level report or other means), and processes enormous amount of analysis rapidly (e.g., by employing parallel processing). A score refers to a level of interest indicating how data from a sensor during a recipe step sensor may affect a quality of a product or a health of a semiconductor manufacturing tool; [0076-0077], As the maximum value of the statistical tests is lower, a corresponding trace data has higher operational interest. In other words, a low score value (e.g., 0.1) indicate an aberrant condition of a corresponding tool. The level of the operational interest refers to a degree of influence affecting a wafer quality or tool operation, e.g., how stable a tool is or how well matched a tool is. The computing device 1600 generates a score, e.g., results from a single statistical test or maximum, minimum, average or other combinations of the statistical tests, which indicates the level of the operational significance, and assigns the generated score to each cell. The computing device 1600 places the score in a corresponding cell of a heat map. Engineers or managers use scores in the cell to evaluate a semiconductor manufacturing tool performance. For example, higher scores represent healthier tool behaviors; [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report; [0093], FIG. 5 illustrates a detailed view of an exemplary engineer's report generated in accordance with the invention. The engineer's report is a summary of a small (e.g., 1) or large number (e.g., on the order of 10.sup.6) of heat maps. Each section 500, e.g., a section labelled "Tool_2" in the exemplary engineer's report in FIG. 5, represents a chamber in a semiconductor manufacturing tool. In the engineer's report, rows such as rows 510 per a section represent interesting rows (i.e., interesting sensors) in chamber-recipe reports. Thus, the engineer's report assists an engineer in knowing which chambers and recipes warrant attention. Many individual chamber recipe reports may be generated for one engineer; [0094], FIG. 6 illustrates a section of an exemplary engineer's report on the left and portions of four exemplary detailed reports on the right; [0133], a score in a heat map reflect how much a corresponding tool behavior (e.g. sensor data) affects a product quality (e.g., yield rate). In other words, the score in the heat map describes a degree of influence that a tool behavior affects a product quality. The computing device 1600 measure how much the tool behavior affects the product quality, e.g., by obtaining the process trace data (e.g., pressures, temperatures, etc.) from sensors in a tool, performing one or more statistical tests (e.g., mutual information, etc.) on the process trace data and product quality measurements and summarizing results of the statistical tests (e.g., designating an average of the results as a score). A higher score may mean that a change in a tool's behavior (e.g. sensor data) is associated with a better quality product. In other words, as a tool has a higher score, the tool operates consistently according to its operation specification and generates a better product; see also [0078])
However, Aharoni in view of Kuyel fails to expressly disclose the summary of performance is based on a weighted combination, wherein the weighted combination is indicated by the performance data. In the same field of endeavor, Kavaklioglu teaches:
the summary of performance is based on a weighted combination, wherein the weighted combination is indicated by the performance data (Kavaklioglu Figs. 1-10; [0040], a particular sub-unit may be considered more important to the area than another sub-unit, and is weighted accordingly; [0041], a device that has a low frequency of failure may be weighted lower than a device with a high frequency of failure. The impact and the frequency of failure may be quantified, with the product of the impact and frequency of failure resulting in the weighting value. The evaluation of impact and frequency of failure may be based on a variety of factors, including, but not limited to, process information, on-line monitoring information, historical information, maintenance information, diagnostic information, and heuristic information based on experience of process plant personnel; [0042], aggregation routine 60 may acquire weighting values related to each device, loop, sub-unit, unit, area, plant, etc. within a group by receiving each weighting value from another source or by creating each weighting value based on information from a variety of sources; [0043], the index aggregation routine 60 may receive information relating to each device, loop, sub-unit, unit, area, plant, etc within a group to evaluate the impact and frequency of failure of each asset within the group, and to further create a weighting value for each asset within the group. The information may include process information, on-line monitoring information, historical information, maintenance information, diagnostic information, and heuristic information as described above. Accordingly, the index aggregation routine 60 is communicatively coupled to model generation routines 56, control routines 62, maintenance system applications 64, data historians 66, diagnostic routines 68, or other data sources as shown in FIG. 2. Each of the various types of information may be used to evaluate the impact and/or frequency of failure of an asset within a group of assets. For example, historical information, diagnostic information and maintenance information may provide information regarding previous failures of a device, while historical information, process information, on-line monitoring information and heuristic information may provide information on the impact of past failures on the group or the predicted impact of a failure on the group. Of course, it should be recognized that the weighting values may be created in a similar manner using other routines or systems within the process plant, including the asset utilization expert 50 or the index generation routine 51, or created outside the process plant; [0045], weighting values may be acquired by receiving the weighting values, by receiving impact and frequency of failure information to create the weighting values; Using the weighting values and indices, the index aggregation routine 60 may create an aggregate index pertaining to the overall status of the group, such as an aggregate health index, an aggregate utilization index, an aggregate performance index or an aggregate variability index. The index aggregation routine 60 may calculate the aggregate index as a weighted average according to the following general equation: u = i = 1 n .times. w i .times. u i i = 1 n .times. w i ( 1 ) ##EQU1## [0046] wherein: [0047] u=the aggregate index of the group [0048] n=the number of assets within the group [0049] h.sub.i=the index for the i.sup.th asset [0050] w.sub.i=the weight of the i.sup.th asset; [0053], the index aggregation routine 60 acquires indices pertaining to the status of each asset within a group and acquires weighting values pertaining to the importance (e.g., criticality, priority) of each asset within a particular group to create an aggregate index pertaining to the overall group. For example, an area may include several devices, loops, sub-units and units. By acquiring the health index values and weighting values for each device within the area, the overall health of the area may be determined; [0054], each of the indices generated by the index generator routine 51 may be calculated for individual devices; [0063], FIG. 3 is an exemplary depiction of a display representing a unit 100 within a process control system that may be displayed by the GUI; [0064], the GUI display shown in FIG. 3 also includes a plurality of index names and values 150. In particular, the index names and values 150 include a performance index, a health index, a variability index and a utilization index)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the summary of performance is based on a weighted combination, wherein the weighted combination is indicated by the performance data as suggested in Kavaklioglu into Aharoni in view of Kuyel. Doing so would be desirable because process control systems, like those used in chemical, petroleum or other processes, typically include one or more centralized or decentralized process controllers communicatively coupled to at least one host or operator workstation and to one or more process control and instrumentation devices, such as field devices, via analog, digital or combined analog/digital buses (see Kavaklioglu [0002]). Because, for the most part, different personnel are interested in the status of different hierarchical levels within or among process plants, various systems within a process plant monitor and report the status of various devices that are connected to the process control systems of the plant, such as the relative health, performance, utilization, variability of the devices. For example, various systems may monitor the individual devices within a process plant. However, beyond the device level, the problem with this approach is that there are thousands of devices in a typical plant and the status of any single device generally cannot be used to determine the overall status of the loop, sub-unit, unit, area or process plant where the device is found (see Kavaklioglu [0008]). Some solutions exist for determining the status of devices, loops, sub-units, units, area and/or plants (see Kavaklioglu [0009]). However, within a typical process plant, some assets are considered more important than other assets within a group of assets. For example, some devices are considered more critical to the larger loop, sub-unit, unit, area, etc. of which the devices are a part. If such a device were to fail, it would have more of an impact on the loop, sub-unit, unit, area, etc. than if the other devices were to fail. Such a device would therefore deserve priority over the other devices. In turn, the remaining devices may have varying degrees of importance within the loop, sub-unit, unit or area. Likewise, some loops are more important than others among a group of loops interconnected to form a sub-unit, unit, area, etc. Similar situations exist among sub-units, units, area and even among plants. The importance of assets among a group of assets may greatly affect the overall status of the group. However, in the past, the varying degrees of importance among assets were not necessarily taken into account when determining the overall status of a group of assets (see Kavaklioglu [0010]).
However, Aharoni in view of Kuyel in further view of Kavaklioglu fails to expressly disclose violations of best known methods. In the same field of endeavor, Huang teaches:
violations of best known methods (Huang Figs. 1-7; [0024], the recipe report card editor may integrate a plurality of input data sources (e.g., recipe, tool data, process data, best known methods, etc.) into a single location; [0037], process report card section 216 may be pre-established by the tool manufacturer based on best-known methods. In another embodiment, process report card section 216 may be established and/or modified by the company's engineers based on knowledge, experience, and criteria established by the company; [0038], criteria section 214 may include a status section 218, which may be employed to define the condition of process report card section 216. In an embodiment, status section 218 may incorporate ranges that may define the statuses. In an embodiment, the ranges may be defined based on best-known methods. In another embodiment, the ranges may be configured based on guidelines established by a company's practices. In an embodiment, status section 218 may be automatically and/or manually entered. In an example, the recipe report card module may have an intelligence that is capable of deriving the status for each of the criteria in process report card section 216 by analyzing the data collected during the execution of the recipe. In another example, one or more criteria may require human intervention)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated violations of best known methods as suggested in Huang into Aharoni in view of Kuyel in further view of Kavaklioglu. Doing so would be desirable because recipes have long been employed by the semiconductor industry to create new devices (e.g., MEMS, chips, etc.). Different recipes may be utilized to perform substrate processing. Recipes tend to be complex and are usually created based on the knowledge and/or experience of the engineers (see Huang [0001]). Usually, a successful execution of a recipe may be dependent upon other variables beside the recipe. Other variables may include, but are not limited to, the condition of the wafers, the chamber condition of the processing system, the wafer environment, and the likes. As a result, the engineers not only have to be knowledgeable about the recipe but may also be required to understand how the recipe may interact with other variables (see Huang [0003]). The engineer may be required to understand the recipe and how the recipe may interact with other variables that may affect the execution of the recipe. Unfortunately, the skill level and experience of an engineer may vary and the `quality` of a recipe may reflect accordingly. Even if the engineer has the skill and experience, the engineer may not be able to account for all the different variables in the creation of a new recipe and/or the modification of a current recipe (see Huang [0004]). A problem may arise during the execution of a recipe. The ability to analyze the data in order to debug the problem may also be dependent upon the engineer's experience and his ability to pinpoint the problem given the different variables that may cause the problem (see Huang [0005]). A user-friendly system designed to integrate a plurality of data sources (e.g., recipe, tool data, sensor data, best known method, etc.) under a single recipe report card framework. Also, the recipe report card framework includes an interactive criteria section that enables recipe evaluation. Further, the recipe report card framework includes an expert guide, such as a help section, to provide guidance in recipe analysis. In addition, the recipe report card framework provides a collaborative infrastructure for creating, modifying, and/or validating a recipe (see Huang [0019]). The recipe report card may incorporate industry standards, best-known methods, expert knowledge, and/or past experience to determine the signal parameters and the criteria for each signal parameters that may affect the success of a recipe execution (see Huang [0022]).
Regarding claims 15 and 20, claims 15 and 20 contain substantially similar limitations to those found in claim 7. Consequently, claims 15 and 20 are rejected for the same reasons.
Regarding claim 8, Aharoni in view of Kuyel in further view of Kavaklioglu in further view of Huang teaches all the limitations of claim 7, further comprising:
wherein the combination is based on a severity of the violations, a frequency of the violations, and a historical impact of a violation on a chamber performance (Aharoni Figs. 1-21; [0072], the present invention analyzes each sensor individually, provides linked heat maps (e.g., clicking a cell in a heat map invokes a lower-level report (i.e., detailed report)), aggregates multiple analyses (e.g., by averaging scores in a lower-level report or other means), and processes enormous amount of analysis rapidly (e.g., by employing parallel processing). A score refers to a level of interest indicating how data from a sensor during a recipe step sensor may affect a quality of a product or a health of a semiconductor manufacturing tool; [0076-0077], As the maximum value of the statistical tests is lower, a corresponding trace data has higher operational interest. In other words, a low score value (e.g., 0.1) indicate an aberrant condition of a corresponding tool. The level of the operational interest refers to a degree of influence affecting a wafer quality or tool operation, e.g., how stable a tool is or how well matched a tool is. The computing device 1600 generates a score, e.g., results from a single statistical test or maximum, minimum, average or other combinations of the statistical tests, which indicates the level of the operational significance, and assigns the generated score to each cell. The computing device 1600 places the score in a corresponding cell of a heat map. Engineers or managers use scores in the cell to evaluate a semiconductor manufacturing tool performance. For example, higher scores represent healthier tool behaviors; [0091] The graph 330 represents current trace data obtained from sensors in the present current data collection time period. In the graphs 320-330, each dot represents a wafer. In the graph 320, data are arranged between 0 and 40 and thus, there is no aberrant wafer. However, in the graph 330, six dots between 14.00 and 15.00 shown along X-axis values of the graph 330 are arranged between 130 and 140 along Y-axis values. Thus, these six dots represents a clear indication that the behavior of the tool has changed dramatically and suggests that corresponding six wafers may be potentially defective. A graphs 320 and 330 show data from all nominally equivalent tools. The graphs 340 and 350 show data from one of those tools. A detailed report including the graphs 320-370 can be accessed from a chamber-recipe or engineer's report. The chamber-recipe and engineer's report are created and viewed in a context of one specific "focus" chamber--that is, the particular chamber for which the detailed report is generated. According to one embodiment of the invention, the scales for all of the plots are generated automatically. When data from all chambers is plotted together, the scale is determined to allow a user to see all of the data from all chambers. In some cases that scale may make it difficult for the user to see the behaviour of the focus chamber in the trend chart. For instance, there may be a significant drift in a trend for the focus chamber from 10 to 20. If there is another chamber that has data ranging from 5 to 500, then it would be hard for the user to see the drift from 10 to 20 on a plot that is scaled to show data ranging up to 500. For this reason, the graphs 340 and 350 illustrates the trend for the "focus chamber" alone. A graph 360 represents histogram of wafers' aggregated time series behaviour in the past. A graph 370 represents histogram of wafers' aggregated time series behaviour in the present. The header information 380 is a part of the chamber-recipe report. The detailed report may also include its own header information such as header information 1000 in shown FIG. 12. The header information 380 may include, but is not limited to, a creation date of the chamber-recipe report 290, an average score of trace data in the report 290 and the number of wafers presented in the report 290 (frequency data shown); [0127], If at step 2515 a user determines that the signals in the reports lead to maintenance, the user at step 2530 determines whether accessing or using tools associated with the signals should be prohibited, e.g., based on a severity of the signals; e.g., based on scores corresponding to the signals; [0133] According to one embodiment of the present invention, a score in a heat map reflect how much a corresponding tool behavior (e.g. sensor data) affects a product quality (e.g., yield rate). In other words, the score in the heat map describes a degree of influence that a tool behavior affects a product quality. The computing device 1600 measure how much the tool behavior affects the product quality, e.g., by obtaining the process trace data (e.g., pressures, temperatures, etc.) from sensors in a tool, performing one or more statistical tests (e.g., mutual information, etc.) on the process trace data and product quality measurements and summarizing results of the statistical tests (e.g., designating an average of the results as a score). A higher score may mean that a change in a tool's behavior (e.g. sensor data) is associated with a better quality product. In other words, as a tool has a higher score, the tool operates consistently according to its operation specification and generates a better product.see also [0078], [0092-0094])
Kavaklioglu further teaches:
the weighted combination is based on a severity of the violations, a frequency of the violations, and a historical impact of a violation (Kavaklioglu Figs. 1-10; [0040], a particular sub-unit may be considered more important to the area than another sub-unit, and is weighted accordingly; [0041], a device that has a low frequency of failure may be weighted lower than a device with a high frequency of failure. The impact and the frequency of failure may be quantified, with the product of the impact and frequency of failure resulting in the weighting value. The evaluation of impact and frequency of failure may be based on a variety of factors, including, but not limited to, process information, on-line monitoring information, historical information, maintenance information, diagnostic information, and heuristic information based on experience of process plant personnel; [0042], aggregation routine 60 may acquire weighting values related to each device, loop, sub-unit, unit, area, plant, etc. within a group by receiving each weighting value from another source or by creating each weighting value based on information from a variety of sources; [0043], the index aggregation routine 60 may receive information relating to each device, loop, sub-unit, unit, area, plant, etc within a group to evaluate the impact and frequency of failure of each asset within the group, and to further create a weighting value for each asset within the group. The information may include process information, on-line monitoring information, historical information, maintenance information, diagnostic information, and heuristic information as described above. Accordingly, the index aggregation routine 60 is communicatively coupled to model generation routines 56, control routines 62, maintenance system applications 64, data historians 66, diagnostic routines 68, or other data sources as shown in FIG. 2. Each of the various types of information may be used to evaluate the impact and/or frequency of failure of an asset within a group of assets. For example, historical information, diagnostic information and maintenance information may provide information regarding previous failures of a device, while historical information, process information, on-line monitoring information and heuristic information may provide information on the impact of past failures on the group or the predicted impact of a failure on the group. Of course, it should be recognized that the weighting values may be created in a similar manner using other routines or systems within the process plant, including the asset utilization expert 50 or the index generation routine 51, or created outside the process plant; [0045], weighting values may be acquired by receiving the weighting values, by receiving impact and frequency of failure information to create the weighting values; Using the weighting values and indices, the index aggregation routine 60 may create an aggregate index pertaining to the overall status of the group, such as an aggregate health index, an aggregate utilization index, an aggregate performance index or an aggregate variability index. The index aggregation routine 60 may calculate the aggregate index as a weighted average according to the following general equation: u = i = 1 n .times. w i .times. u i i = 1 n .times. w i ( 1 ) ##EQU1## [0046] wherein: [0047] u=the aggregate index of the group [0048] n=the number of assets within the group [0049] h.sub.i=the index for the i.sup.th asset [0050] w.sub.i=the weight of the i.sup.th asset; [0053], the index aggregation routine 60 acquires indices pertaining to the status of each asset within a group and acquires weighting values pertaining to the importance (e.g., criticality, priority) of each asset within a particular group to create an aggregate index pertaining to the overall group. For example, an area may include several devices, loops, sub-units and units. By acquiring the health index values and weighting values for each device within the area, the overall health of the area may be determined; [0054], each of the indices generated by the index generator routine 51 may be calculated for individual devices; [0063], FIG. 3 is an exemplary depiction of a display representing a unit 100 within a process control system that may be displayed by the GUI; [0064], the GUI display shown in FIG. 3 also includes a plurality of index names and values 150. In particular, the index names and values 150 include a performance index, a health index, a variability index and a utilization index)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the weighted combination is based on a severity of the violations, a frequency of the violations, and a historical impact of a violation as suggested in Kavaklioglu into Aharoni in view of Kuyel in further view of Huang. Doing so would be desirable because process control systems, like those used in chemical, petroleum or other processes, typically include one or more centralized or decentralized process controllers communicatively coupled to at least one host or operator workstation and to one or more process control and instrumentation devices, such as field devices, via analog, digital or combined analog/digital buses (see Kavaklioglu [0002]). Because, for the most part, different personnel are interested in the status of different hierarchical levels within or among process plants, various systems within a process plant monitor and report the status of various devices that are connected to the process control systems of the plant, such as the relative health, performance, utilization, variability of the devices. For example, various systems may monitor the individual devices within a process plant. However, beyond the device level, the problem with this approach is that there are thousands of devices in a typical plant and the status of any single device generally cannot be used to determine the overall status of the loop, sub-unit, unit, area or process plant where the device is found (see Kavaklioglu [0008]). Some solutions exist for determining the status of devices, loops, sub-units, units, area and/or plants (see Kavaklioglu [0009]). However, within a typical process plant, some assets are considered more important than other assets within a group of assets. For example, some devices are considered more critical to the larger loop, sub-unit, unit, area, etc. of which the devices are a part. If such a device were to fail, it would have more of an impact on the loop, sub-unit, unit, area, etc. than if the other devices were to fail. Such a device would therefore deserve priority over the other devices. In turn, the remaining devices may have varying degrees of importance within the loop, sub-unit, unit or area. Likewise, some loops are more important than others among a group of loops interconnected to form a sub-unit, unit, area, etc. Similar situations exist among sub-units, units, area and even among plants. The importance of assets among a group of assets may greatly affect the overall status of the group. However, in the past, the varying degrees of importance among assets were not necessarily taken into account when determining the overall status of a group of assets (see Kavaklioglu [0010]).
Regarding claim 16, claim 16 contains substantially similar limitations to those found in claim 8. Consequently, claim 16 is rejected for the same reasons.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of Kuyel in further view of Unterguggenberger et al. (US 20240192653 A1, published 06/13/2024), hereinafter Unterguggenberger. Examiner Note: Disclosure in Unterguggenberger used for rejection is supported in provisional application 63/201,134 filed on 04/14/2021.
Regarding claim 21, Aharoni in view of Kuyel teaches all the limitations of claim 1, further comprising:
providing, via the graphical user interface (GUI), a graphical encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run (Aharoni Figs. 1-21; [0072], the present invention analyzes each sensor individually, provides linked heat maps (e.g., clicking a cell in a heat map invokes a lower-level report (i.e., detailed report)), aggregates multiple analyses (e.g., by averaging scores in a lower-level report or other means), and processes enormous amount of analysis rapidly (e.g., by employing parallel processing). A score refers to a level of interest indicating how data from a sensor during a recipe step sensor may affect a quality of a product or a health of a semiconductor manufacturing tool; [0092], FIG. 4 illustrates a hierarchical view of three reports, the chamber-recipe report 290, an engineer's report 430 and a manager's report 400. In this hierarchical view, the chamber-recipe report 290 is the lowest-level report; [0093], FIG. 5 illustrates a detailed view of an exemplary engineer's report generated in accordance with the invention. The engineer's report is a summary of a small (e.g., 1) or large number (e.g., on the order of 10.sup.6) of heat maps. Each section 500, e.g., a section labelled "Tool_2" in the exemplary engineer's report in FIG. 5, represents a chamber in a semiconductor manufacturing tool. In the engineer's report, rows such as rows 510 per a section represent interesting rows (i.e., interesting sensors) in chamber-recipe reports. Thus, the engineer's report assists an engineer in knowing which chambers and recipes warrant attention. Many individual chamber recipe reports may be generated for one engineer; [0094], FIG. 6 illustrates a section of an exemplary engineer's report on the left and portions of four exemplary detailed reports on the right; [0133], a score in a heat map reflect how much a corresponding tool behavior (e.g. sensor data) affects a product quality (e.g., yield rate). In other words, the score in the heat map describes a degree of influence that a tool behavior affects a product quality. The computing device 1600 measure how much the tool behavior affects the product quality, e.g., by obtaining the process trace data (e.g., pressures, temperatures, etc.) from sensors in a tool, performing one or more statistical tests (e.g., mutual information, etc.) on the process trace data and product quality measurements and summarizing results of the statistical tests (e.g., designating an average of the results as a score); see also [0076-0078])
However, Aharoni in view of Kuyel fails to expressly disclose providing, via the graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run. In the same field of endeavor, Unterguggenbergerteaches:
providing, via the graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run (UnterguggenbergerFigs. 1-10; [0095], the particular machine-readable code may be attached to or displayed by a semiconductor manufacturing tool or a device communicating with the MR control system; [0114], the MR control system causes the machine-readable code to be presented, for example, on a display screen associated with the MR control system; [0149], the machine-readable code may be affixed to an external portion of the semiconductor manufacturing tool and/or may be presented via a display of a device executing the MR control system. In instances in which the input includes capture of image data that includes a machine-readable code, the MR headset can decode the machine-readable code to obtain an identifier embedded within the machine-readable code. In some embodiments, the identifier is uniquely associated with a particular set of operational parameters with respect to at least one or more components of the semiconductor manufacturing tool that the MR control system is communicating with; [0155], Referring back to process 500 in FIG. 5A, in some embodiments, at 508, the MR headset may request a set of operational information from the semiconductor manufacturing tool from the MR control system. In an instance in which the input received at block 506 is a selection of one or more selectable user interface elements, the MR headset can request the indicated set of operational information by transmitting identifier(s) of the selected user interface elements to the MR control system. For example, in an instance in which a selected user interface element corresponds to “Platform pressures” from a menu of available sets of operational information shown in FIG. 6B, the MR headset can transmit the command that the “Platform pressures” element was selected from the menu to the MR control system; [0156], In an instance in which the input received at block 506 is image data that includes a machine-readable code such as a QR code scan, the MR headset can transmit a decoded identifier retrieved from the machine-readable code to the MR control system. In some embodiments, upon receiving the command from the MR headset, the MR control system can then identify a set of operational information associated with the identifier; see also [0186])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated providing, via the graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run as suggested in Unterguggenbergerinto Aharoni in view of Kuyel. Doing so would be desirable because Semiconductor manufacturing equipment may be very complex, and may also be physically large and/or may include opaque walls, which may make it difficult to determine a state of the equipment from visual inspection. Status information associated with operation of semiconductor manufacturing equipment (e.g., a current operational status, current sensor values, etc.) may generally be presented on a display screen (see Unterguggenberger [0002]). By rendering operational information in an MR environment, the systems, methods, and computer products described herein may improve operation of a semiconductor manufacturing tool by reducing repair times, reducing tool down time, improving access to sensor data, or the like (see Unterguggenberger [0059]). Embodiments of the present disclosure improve the control platform of semiconductor manufacturing tool by providing an unrestricted display and control environment and introducing fully customizable control mechanisms to allow operators to generate situation-based control GUI (see Unterguggenberger [0061]). Additionally, the system of Unterguggenberger would improve the system of Aharoni by providing a simple and convenient mechanism to access desired information, thereby saving the user time and increasing user satisfaction.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of Kuyel in further view of Matsushita et al. (US 20050251365 A1, published 11/10/2005), hereinafter Matsushita.
Regarding claim 22, Aharoni in view of Kuyel teaches all the limitations of claim 1. However, Aharoni in view of Kuyel fails to expressly disclose identifying one or more substrates for which one or more anomalies occurred; and displaying, via the GUI, a recommendation for the one or more substrates to be sampled. In the same field of endeavor, Matsushita teaches:
identifying one or more substrates for which one or more anomalies occurred; and displaying, via the GUI, a recommendation for the one or more substrates to be sampled (Matsushita Figs. 1-15; [0045], FIG. 2 shows an example of a display showing a tendency within a substrate surface of a failure lot that is extracted by the extraction module 22 on a display 2a at the user terminal 2. FIG. 2 shows, as an example, a failure lot identification number (ID) for a lot where there is a periphery failure, and a pattern 46 for a plurality of failure unit regions. As to the pattern 46 for a plurality of failure unit regions shown on a field for a typical substrate map, a portion marked with color on a substrate 27a represents a failure unit region 44. A portion marked with white represents a pass unit region 45. A failure lot identification number (ID) for a lot extracted by the extraction module 22 is displayed in a field for failure lot ID as a failure lot which has a tendency for failures (failure pattern) within a surface of a substrate where failures represented by the failure unit region 44 are concentrated along the periphery of the substrate. FIG. 2 shows an example where ten lots in total are extracted as failure lots having the same tendencies for failures within the substrate surfaces, the ten lots being denoted as failure lot identification number "#1345," failure lot identification number "#2316," failure lot identification number "#2684," . . . failure lot identification number "#3671."; [0117], In Step ST64, an extraction module 22 extracts, from the yield database 60, the failure lot identification information corresponding to the failure lot where the yield is less than or equal to an arbitrary threshold. The failure lot identification information thus extracted is displayed on a display 2a of the user terminal 2. The user terminal 2 accesses the yield database 60 to display failure unit region patterns created in a plurality of lots on a field for a typical substrate, and prompts a user to extract the failure lot identification information; [0118], In Step ST65, the estimation module 23 selects data in the 2-level orthogonal array corresponding to the extracted failure lot identification information, from an orthogonal array database 19. Further, in Step ST66, the estimation module 23 estimates a failure lot group using an orthogonal array selected in Step ST65, and performs a tool-difference analysis using the orthogonal array in Step ST67; [0119], In Step ST68, the chart generating module 6 reads, from the tool database 4, the history information of the manufacturing tool groups 3a, 3b, 3c, 3d. Further, the chart generating module 6 calculates the test value of a process for an abnormal candidate or a tool for an abnormal candidate for a failure lot group; [0125], FIG. 14A is an example displayed on the display 2a showing the lot identification information on the low yield lot group extracted by the extraction module 22. In the display 2a, the lot numbers "#5423," "#5562," "#5872," and "#8724," for example, are displayed in the ascending order, as low yield lot identification information, from the top row to the bottom row; [0126], FIG. 14B is an example in which the failure lot identification information with respect to the factors from "1" to "15", respectively allocated to the trial numbers "1" to "12", are displayed on the display 2a at the user terminal 2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated identifying one or more substrates for which one or more anomalies occurred; and displaying, via the GUI, a recommendation for the one or more substrates to be sampled as suggested in Matsushita into Aharoni in view of Kuyel. Doing so would be desirable because the present invention relates to a system and a method for identifying a manufacturing tool causing a fault, which analyzes the causes of the reason for a decreased product yield in a manufacturing method and manufacturing of industrial products (see Matsushita [0003]). When distributions within the surfaces of clustering faults, which may be caused by a plurality of tools, have patterns similar to one another, it is difficult to distinguish the tools causing the fault from one another using only fault unit region patterns indicating the distribution of failure locations on substrates. (see Matsushita [0005]). The system of Matsushita would improve the system of Aharoni by providing a convenient and user-friendly method of displaying desired anomaly information to a user, thereby simplifying and improving the fault diagnosis process.
Response to Arguments
The Examiner acknowledges the Applicant’s amendments to claims 1, 2, 4, 5, 7-10, 12, 15, 17, 19, and 20, the addition of claims 21 and 22, and the cancellation of claims 3 and 11. The corrections to claims 1, 2, 5, 8-10, 16, and 17 are approved and the previous objections to these claims are respectfully withdrawn. A terminal disclaimer has been filed and the nonstatutory double patenting rejection of the claims is respectfully withdrawn. The interpretation of the claims under 35 U.S.C. 112(f) is respectfully withdrawn. As discussed above, the claims stand rejected under 35 U.S.C. 112(b).
Regarding independent claim 1, the Applicant alleges that Aharoni as described in the previous Office action, does not explicitly teach a first axis, corresponding to a first division into a first plurality of subsystems of the first chamber, wherein a subsystem comprises a plurality of related components associated with a function of the first chamber, and wherein the first plurality of subsystems comprises at least one of a temperature control subsystem, a gas delivery subsystem, a RF delivery subsystem, or a pressure subsystem; a plurality of icons each indicating performance of the chamber in association with one of the first plurality of subsystems and one of the second plurality of groups of data, as has been amended to the claim. Examiner has therefore rejected independent claim 1 under 35 U.S.C § 103 as unpatentable over Aharoni in view of Kuyel.
Similar arguments have been presented for claims 9 and 17 and thus, Applicant’s arguments are not persuasive for the same reasons.
Applicant states that the dependent claims recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding the independent claims. However, as discussed above, Aharoni in view of Kuyel is considered to teach the independent claims, and consequently, the dependent claims are rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cheon (US 20230237412 A1) see Figs. 1-8 and [0178].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T REPSHER III whose telephone number is (571)272-7487. The examiner can normally be reached Monday - Friday, 8AM-5PM EST.
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/JOHN T REPSHER III/ Primary Examiner, Art Unit 2143
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