DETAILED ACTION
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 Objections
In claim 1, consider --determining a mix-in volume of fresh polishing slurry associated with the forecasted slurry consumption value; and
operating a supply tank containing polishing slurrywherein operating the supply tank containing polishing slurry includes:
determining whether a liquid level of the polishing slurry is below the refill line by a continuous liquid level sensor assembly mounted on the supply tank--.
In claim 8 consider --in response to the liquid level being below the refill line, forming a mixed polishing slurry by adding a mix-in volume of fresh polishing slurry to the supply tank;
forming a second polishing slurry by aging the mixed polishing slurry--
In claim 10, consider using --polishing slurry historical data-- instead of “polishing liquid historical data”, for more consistency.
In claim 13, consider -- wherein determining the refill line and the mix-in volume includes determining the refill line and the mix-in volume via the trained machine learning model based on at least one of: polishing slurry conductivity data, polishing slurry rate of increase data or supply lifetime data--
In claim 14, consider --determining the refill line and the mix-in volume includes determining the refill line and the mix-in volume via the trained machine learning model based on at least one of: a number of polishing apparatuses supplied by the supply tank or production volume of each of a plurality of polishing apparatuses supplied by the supply tank--.
In claim 16, consider --generate a second polishing slurry by aging a mixture of the polishing slurry and the fresh polishing slurry in the third tank--
Examiner understands that the term “slurry” may be considered by the applicant to be plural (the term “slurries” can also be used to represent plural slurry), and if that is the case, the applicant does not have to use terminology, suggested in the objection above, such as --a second polishing slurry--.
Claim Interpretation
The terms “continuous liquid level sensor assembly” (claim 1), and “liquid level sensor assembly” (claims 8, 15, 17-19) is not interpreted under 35 USC 112(f) despite the nonce term “assembly” because the term “sensor” provides sufficient structure.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hiraoka (US 6457852 B1)
With respect to claim 1, Hiraoka discloses:
A method, comprising:
determining a forecasted slurry consumption value (col 10 lines 39-48, calculating slurry consumption necessary based on number of wafers and flow rate);
determining a refill line associated with the forecasted slurry consumption value (col 10 lines 49-65, specifically the preparation start level, which is associated with the forecasted slurry consumption as the amount of preparation of a new volume of slurry is based on the forecasted slurry consumption);
determining a mix-in volume associated with the forecasted slurry consumption value (col 10 lines 39-48, amount of new slurry needed to be added, taking into account forecasted consumption and the number of wafers/flow rate)
and operating a supply tank containing polishing slurry (supply tank 12a, 12b, containing slurry is supplied to CMP units 18a, 18b; col 4 lines 22-35, fig. 1);
the operating being according to the refill line and the mix-in volume (the operation of the tank is according to the refill line as in col 10 lines 49-65, which depends on the preparation start level), the operating including:
determining whether liquid level of the polishing slurry is below the refill line by a continuous liquid level sensor assembly mounted on the supply tank (col 6 lines 36-46, referring to sensors 30a, 30b, fig. 1 on tanks 12a, 12b, used to determine the residual level as in col 10 lines 39-65, which is used to determine whether to prepare a new batch of slurry based on the refill line/preparation start level, sensor can measure continuously change in height);
in response to the liquid level of the polishing slurry being below the refill line, adding the mix-in volume of fresh polishing slurry to the supply tank (col 10 lines 49-65, provides that in response to the slurry being below the preparation start level, the preparation of a new batch of slurry is done with respect to the other tank [i.e. not the one with the low level below the preparation start level], this indicates that the tanks alternate, therefore, the refill of the first low level tank will also subsequently occur later when the second tank also reaches a low level, as in col 5 lines 7-20, therefore, the refill and mix in of new slurry will eventually, be done in response to the slurry being consumed in the first tank, after the second tank is also consumed as well [the switch to the second tank being in response to the slurry being below the refill line in the first tank], for continuous slurry supply as in col 4 lines 47-56, examiner notes that the claim does not require an immediate response);
and supplying the polishing slurry to a polishing apparatus that is operable to polish a surface of a semiconductor wafer (col 4 lines 29-37 provides slurry is fed to CMP machines 18a, 18b, fig. 1, CMP machines polish semiconductor as in col 1 lines 4-10).
With respect to claim 2, Hiraoka discloses the limitations of claim 1 above, and further discloses prior to determining the forecasted slurry consumption value, operating the supply tank according to a first refill line and a first mix-in volume (the tanks operate alternately continuously as in col 4 lines 47-56, therefore there is a refill line and mix in volume for each cycle of operation, before a new batch of slurry is prepared [in which the amount prepared takes into account the forecasted slurry consumption]); and prior to operating the supply tank according to the refill line and the mix-in volume: replacing the first refill line with the refill line; and replacing the first mix-in volume with the mix-in volume (for each batch, as in col 10 lines 39-48, calculating slurry consumption necessary based on number of wafers and flow rate to determine the mix in volume of new slurry taking into account the residual; for the refill line, the level also takes into account the flow rate as in col 10 lines 49-65 and col 11 lines 10-21, thus providing for a new refill line that takes into account the flow rate for continuous processing).
With respect to claim 4, Hiraoka discloses the limitations of claim 2 above, and further discloses wherein replacing the first mix-in volume includes replacing the first mix-in volume with the mix-in volume that is less than the first mix-in volume in response to a decrease in volume of wafers forecasted to be polished by the polishing apparatus (for a future polishing batch, the amount of slurry added is based on the number of wafers in col 10 lines 39-48, and if the future number of wafers being polished decreases, the mix in volume would decrease, assuming the same flow rate).
With respect to claim 7, Hiraoka discloses the limitations of claim 1 above, and further discloses wherein determining the forecasted slurry consumption value includes determining the forecasted slurry consumption value based on a supply lifetime of the polishing slurry contained in the supply tank (forecasted slurry consumption based on number of wafers and flow rate col 10 lines 39-49; these parameters relate to supply lifetime as in col 4 lines 34-46; further col 4 lines 46-57 correlate the volume of slurry generated to an expiration time).
Claim Rejections - 35 USC § 103
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiraoka (US 6457852 B1) and further in view of Byers (US 20140261824 A1).
With respect to claim 3, Hiraoka discloses the limitations of claim 2 above, however does not explicitly disclose wherein replacing the first refill line includes replacing the first refill line with the refill line that exceeds the first refill line in response to an increase in volume of wafers forecasted to be polished by the polishing apparatus.
Hiraoka, however discloses that the refill line should take into account the flow rate as well as the time needed to prepare a new batch of slurry (col 10 lines 49-65), with the amount of slurry needed to be prepared, as well as timing of preparation based on how many wafers being processed (col 10 lines 25-48, with the parameters stored by the controller, col 11 lines 10-21 also provides that the timing/level at which processing of a new batch begins also depends on this stored information ), and that the machine operates continuously (col 7 lines 7-19).
Byers, in the same field of endeavor, related to slurry processing, teaches of maintain a buffer amount of slurry, which is based on increased demand ([0229],“ In normal operation, the volumes of slurry and/or chemical blend in the feed and distribution modules are kept at volumes (tank levels) that can respond to increased demand by the tools (providing slurry or chemical blend so that the slurry or chemical blend circulates in the circulating loops (global loop) continuously) without running out of slurry or chemical blend and with a sufficient buffer amount so that the blend module can run at its steady-state rate and replace the volume in the distribution tank when the demand increases, and the volumes of raw slurry in the feed module can be replaced, if necessary, in its range of ordinary steady-state operation). Byers teaches that this allows the machine to run for long periods of time with consistency ([0229], “Once the equipment parts and settings are established and the apparatus is running at steady-state, the apparatus can operate for days, weeks or months using the steady-state settings to supply a plurality of tools a consistent, high quality blend of slurry or chemical blend”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Byers, which would have resulted in wherein replacing the first refill line includes replacing the first refill line with the refill line that exceeds the first refill line in response to an increase in volume of wafers forecasted to be polished by the polishing apparatus, to provide for a sufficient amount of buffer slurry in response to an increased demand caused by an increase in the number of wafers, to ensure that there remains a sufficient buffer amount (the refill line) for continuous slurry supply to ensure for consistent quality.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiraoka (US 6457852 B1) in view of Blanchard (US 3797311 A) and Kim (US 20050198912 A1).
With respect to claim 8, Hiraoka discloses A method comprising:
determining a liquid level of polishing slurry contained in a supply tank via a liquid level sensor assembly mounted to the supply tank (col 6 lines 36-46, referring to sensors 30a, 30b, fig. 1 on tanks 12a, 12b, used to determine the residual level as in col 10 lines 39-65, which is used to determine whether to prepare a new batch of slurry based on the refill line/preparation start level),
determining whether the liquid level is below a refill line (col 10 lines 39-65, which is used to determine whether to prepare a new batch of slurry based on the refill line/preparation start level);
in response to the liquid level being below the refill line, forming mixed polishing slurry by adding a mix-in volume of fresh polishing slurry to the supply tank (col 10 lines 49-65, provides that in response to the slurry being below the preparation start level, the preparation of a new batch of slurry is done with respect to the other tank [i.e. not the one with the low level below the preparation start level], this indicates that the tanks alternate, therefore, the refill of the first low level tank will also subsequently occur later when the second tank also reaches a low level, as in col 5 lines 8-20, therefore, the refill and mix in of new slurry will eventually, be done in response to the slurry being consumed in the first tank, after the second tank is also consumed as well [the switch to the second tank being in response to the slurry being below the refill line in the first tank], for continuous slurry supply as in col 4 lines 47-56, examiner notes that the claim does not require an immediate response);
however, does not explicitly disclose the liquid level sensor assembly extending continuously from a first end of the supply tank to a second end of the supply tank; forming second polishing slurry by aging the mixed polishing slurry; and polishing a surface of a semiconductor wafer using the second polishing slurry by a polishing apparatus.
As for liquid level sensor assembly extending continuously from a first end of the supply tank to a second end of the supply tank, Blanchard, reasonably pertinent to the problem being solved of accurate liquid measurement, provides for a sensor that extends continuously from a first end of the supply tank to a second end of the supply tank (sensor 10, fig. 1, see extension from top to bottom of tank 11 in fig. 1, col 5 lines 3-16, sensor is spaced away from wall of the tank such that residue on wall is not likely to affect measurement). Blanchard teaches that this arrangement provides for especially precise measurements at upper and lower extremes of the tank (col 1 lines 39-55).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Blanchard, and have provided a liquid level sensor assembly extending continuously from a first end of the third tank to a second end of the third tank for the purpose of especially precise measurements at tank extremities.
As for forming second polishing slurry by aging the mixed polishing slurry; and polishing a surface of a semiconductor wafer using the second polishing slurry by a polishing apparatus, Hiraoka provides that the polishing slurry is used to polish a semiconductor wafer (col 4 lines 21-37 provides slurry is fed to CMP machines 18a, 18b, fig. 1, CMP machines polish semiconductor as in col 1 lines 4-10). Kim, in the same field of endeavor, related to polishing provides for aging the polishing slurry before use ([0025], during mixing as in [0061]). Kim provides that this stabilizes the slurry before use ([0041]).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Kim, and have formed a second polishing slurry by aging the mixed polishing slurry; before polishing a surface of a semiconductor wafer using the second (aged) polishing slurry by a polishing apparatus, for the purpose of ensuring that the slurry is stable.
Claim(s) 9-11, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiraoka (US 6457852 B1) in view of Blanchard (US 3797311 A) and Kim (US 20050198912 A1), and further in view of Deshpande (US 20220193858 A1).
With respect to claim 9, Hiraoka, as modified, teaches the limitations of claim 8 above, however does not explicitly teach determining the refill line and the mix-in volume via a trained machine learning model. However, Hiraoka discloses that the refill line and mix in volume is determined based on different stored parameters (in col 10 lines 39-48, calculating slurry consumption necessary based on number of wafers and flow rate to determine the mix in volume of new slurry taking into account the residual slurry as well as based on processing information; for the refill line, the level also takes into account the flow rate and processing information as in col 10 lines 49-65 and col 11 lines 10-21).
Deshpande, in the same field of endeavor, related to polishing, teaches of applying machine learning to improve polishing parameters by analyzing results with previous time series data captured for the result of changing one or more polishing parameters ([0011]), including of polishing fluid ([0087-0088]). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008])
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Deshpande, and have determined the refill line and the mix-in volume via a trained machine learning model, for improved results with a complex system.
With respect to claim 10, Hiraoka, as modified, teaches the limitations of claim 9 above, however does not explicitly teach generating the trained machine learning model by training a machine learning model using polishing liquid historical data and labeled data associated with the polishing liquid historical data.
Deshpande further teaches of generating the trained machine learning model by training a machine learning model using polishing liquid historical data and labeled data associated with the polishing liquid historical data (track changes in properties and volumes of polishing fluid, and other data such as disturbances on the surface as in [0087], to adjust different parameters in [0088]). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008])
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Deshpande, and have generated the trained machine learning model by training a machine learning model using polishing liquid historical data and labeled data associated with the polishing liquid historical data, for improved results with a complex system.
With respect to claim 11, Hiraoka, as modified, teaches the limitations of claim 10 above, however does not explicitly teach generating the trained machine learning model by training a machine learning model using tool historical data, wafer production historical data and labeled data associated with the tool historical data and the wafer production historical data.
Deshpande further teaches of generating the trained machine learning model using tool historical data (tool data such as polishing pad characteristic, [0119] previously captured), wafer production historical data (wafer uniformity data as in [0120]) and labeled data associated with the tool historical data and the wafer production historical data ([0119, 0120] provides for other labeled associated data). Deshpande teaches that the data can be used to make changes to polishing fluid ([0127], along with other parameters after analysis). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008])
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Deshpande, and have generating the trained machine learning model by training a machine learning model using tool historical data, wafer production historical data and labeled data associated with the tool historical data and the wafer production historical data, for improved results with control of a complex system with complicated relationships.
With respect to claim 13, Hiraoka, as modified, teaches the limitations of claim 9 above, however does not explicitly teach wherein determining the refill line and the mix-in volume includes determining the refill line and the mix-in volume via the trained machine learning model based on at least one of polishing slurry conductivity data, polishing slurry rate of increase data or supply lifetime data.
Hiraoka discloses that the slurry consumption is calculated based on a supply lifetime data of the slurry (forecasted slurry consumption based on number of wafers and flow rate col 10 lines 39-49; these parameters relate to supply lifetime as in col 4 lines 34-46; further col 4 lines 46-57 correlate the volume of slurry generated to an expiration time).
As mentioned in the rejection of claim 9, Deshpande teaches of applying machine learning to improve polishing parameters by analyzing results with previous time series data captured for the result of changing one or more polishing parameters ([0011]), including of polishing fluid ([0087-0088]). Deshpande also teaches monitoring the polishing fluid ([0087] for further understanding of fluid interactions). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008]).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Deshpande, and have determined the refill line and the mix-in volume via the trained machine learning model based on at least one of polishing slurry conductivity data, polishing slurry rate of increase data or supply lifetime data, by using the supply lifetime information taught by Hiraoka with the machine learning model of Deshpande, for improved results with the better understanding of the interaction between various aspects of the polishing machine.
With respect to claim 14, Hiraoka, as modified, teaches the limitations of claim 9 above, however does not explicitly teach wherein determining the refill line and the mix-in volume includes determining the refill line and the mix-in volume via the trained machine learning model based on at least one of number of polishing apparatuses supplied by the supply tank or production volume of each of a plurality of polishing apparatuses supplied by the supply tank.
Hiraoka teaches of multiple polishing apparatuses (see 18a, 18b, fig. 1, col 4 lines 33-46, fed by tanks 12a, 12b, in fig. 1), with slurry consumption calculated based on the wafers processed in each polishing apparatus (col 4 lines 33-46).
As mentioned in the rejection of claim 9, Deshpande teaches of applying machine learning to improve polishing parameters by analyzing results with previous time series data captured for the result of changing one or more polishing parameters ([0011]), including of polishing fluid ([0087-0088]). Deshpande also teaches monitoring the polishing fluid ([0087] for further understanding of fluid interactions). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008]).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Deshpande, and have determined the refill line and the mix-in volume via the trained machine learning model based on at least one of number of polishing apparatuses supplied by the supply tank or production volume of each of a plurality of polishing apparatuses supplied by the supply tank using aspects disclosed by Hiraoka with the machine learning model of Deshpande, for improved results with the better understanding of the interaction between various aspects of the polishing machine.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiraoka (US 6457852 B1) in view of Blanchard (US 3797311 A), Kim (US 20050198912 A1), and Deshpande (US 20220193858 A1), and further in view of Matsuo (US 20140273753 A1).
With respect to claim 12, Hiraoka, as modified, teaches the limitations of claim 10 above, however does not explicitly teach wherein training the machine learning model includes: training the machine learning model using polishing slurry conductivity historical data.
Matsuo, in the same field of endeavor, related to polishing, teaches of measuring polishing slurry conductivity ([0096], to represent freshness of the polishing fluid), and using the conductivity to adjust polishing parameters ([0097-0099]). Matsuo teaches that this ensures freshness of the slurry to fully utilize the capability of the slurry ([0010]).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka to have measured polishing slurry conductivity as taught by Matsuo, to fully utilize the capability of the slurry.
As mentioned in the rejection of claim 9, Deshpande teaches of applying machine learning to improve polishing parameters by analyzing results with previous time series data captured for the result of changing one or more polishing parameters ([0011]), including of polishing fluid ([0087-0088]). Deshpande also teaches monitoring the polishing fluid ([0087] for further understanding of fluid interactions). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008]).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hiraoka with the teachings of Deshpande, and have training the machine learning model includes: training the machine learning model using polishing slurry conductivity historical data for improved results with the better understanding of the interaction between various aspects of the polishing machine.
Claim(s) 15, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho (KR 101122536 B1), Blanchard (US 3797311 A) and Hiraoka (US 6457852 B1)
With respect to claim 15, Cho discloses A system, comprising:
a first tank operable to store base slurry (first tank 21, fig .1; [0048] to store slurry stock);
a second tank operable to generate polishing slurry including filtered base slurry generated from the base slurry (second tank 31, fig. 1; [0066], filter 24 from tank 21);
a third tank operable to supply the polishing slurry to a polishing apparatus (41, fig. 1; [0077-0078], supplies slurry to CMP machine as in [0082]);
a liquid level sensor assembly mounted to the third tank (load cell 42 indirectly measures liquid level as in [0079-0080])
however does not explicitly teach
the liquid level sensor assembly extending continuously from a first end of the third tank to a second end of the third tank; and a controller in data communication with the liquid level sensor assembly, the controller operable to: replace a first refill line associated with the liquid level sensor assembly with a second refill line that is at a different liquid level than the first refill line; determine whether a liquid level of the polishing slurry in the third tank is below the second refill line; and in response to the liquid level being below the second refill line, add fresh polishing slurry to the third tank. Cho, however teaches of a controller ([0088] for controlling a valve)
As for liquid level sensor assembly extending continuously from a first end of the third tank to a second end of the third tank, Blanchard, reasonably pertinent to the problem being solved of accurate liquid measurement, provides for a sensor that extends continuously from a first end of the supply tank to a second end of the supply tank (sensor 10, fig. 1, see extension from top to bottom of tank 11 in fig. 1, col 5 lines 3-16, sensor is spaced away from wall of the tank such that residue on wall is not likely to affect measurement). Blanchard teaches that this arrangement provides for especially precise measurements at upper and lower extremes of the tank (col 1 lines 39-55).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho with the teachings of Blanchard, and have provided a liquid level sensor assembly extending continuously from a first end of the third tank to a second end of the third tank for the purpose of especially precise measurements at tank extremities.
As for a controller in data communication with the liquid level sensor assembly, the controller operable to: replace a first refill line associated with the liquid level sensor assembly with a second refill line that is at a different liquid level than the first refill line; determine whether a liquid level of the polishing slurry in the third tank is below the second refill line; and in response to the liquid level being below the second refill line, add fresh polishing slurry to the third tank, Hiraoka, in the same field of endeavor, related to polishing teaches of providing two supply tanks (tanks 12a, 12b, fig. 1, analogous to the third tank in that it is the tank from which the CMP machines 18a, 18b are supplied from, the tanks operate alternately continuously as in col 4 lines 47-56), a controller in data communication with the liquid level sensor assembly (control unit 41, fig. 2, col 9 lines 50-64, connected to sensor sensors 30a, 30b, fig. 1 on tanks 12a, 12b, col 6 lines 36-46), the controller operable to: replace a first refill line associated with the liquid level sensor assembly with a second refill line that is at a different liquid level than the first refill line (for each batch, as in col 10 lines 39-48, calculating slurry consumption necessary based on number of wafers and flow rate to determine the mix in volume of new slurry taking into account the residual; for the refill line, the level also takes into account the flow rate as in col 10 lines 49-65 and col 11 lines 10-21, thus providing for a new refill line that takes into account the flow rate for continuous processing), determine whether a liquid level of the polishing slurry in the third tank is below the second refill line; and in response to the liquid level being below the second refill line, add fresh polishing slurry to the third tank (col 10 lines 49-65, provides that in response to the slurry being below the preparation start level, the preparation of a new batch of slurry is done with respect to the other tank [i.e. not the one with the low level below the preparation start level], this indicates that the tanks alternate, therefore, the refill of the first low level tank will also subsequently occur later when the second tank also reaches a low level, as in col 5 lines 8-20, therefore, the refill and mix in of new slurry will eventually, be done in response to the slurry being consumed in the first tank, after the second tank is also consumed as well [the switch to the second tank being in response to the slurry being below the refill line in the first tank], for continuous slurry supply as in col 4 lines 47-56, examiner notes that the claim does not require an immediate response). Hiraoka teaches that this would improve efficiency with continuous supply of slurry (abstract)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho with the teachings of Hiraoka, and have provided a parallel slurry tank arrangement, with a refill line where fresh polishing slurry is added to the third tank, for improved polishing efficiency with continuous slurry supply. As noted above, this would result in a [first] third tank where when the slurry is below the refill line, a parallel [second] third tank would start to be filled, and then as a later response when the second third tank is below the refill line, then the first third tank is then refilled, and where the refill line is adjusted to be different based on the flow rate, to provide for a continuous slurry supply.
With respect to claim 17, Cho, as modified, teaches the limitations of claim 15 above, however does not explicitly teach wherein the liquid level sensor assembly includes a plurality of capacitive liquid level sensors arranged between the first end and the second end.
Blanchard, provides for a sensor assembly with includes a plurality of capacitive liquid level sensors arranged between the first end and the second end (multiple capacitors coaxial capacitors formed from outer electrodes 14a, 18a and inner electrodes 14b and 18b, figs. 1, 1a to 1c, col 5 line 50 to col 6 line 3). Blanchard teaches that this arrangement provides for especially precise measurements at upper and lower extremes of the tank (col 1 lines 39-55).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho with the teachings of Blanchard, and have provided a plurality of capacitive liquid level sensors arranged between the first end and the second end for the purpose of especially precise measurements at tank extremities.
With respect to claim 18, Cho, as modified, teaches the limitations of claim 17 above, however does not explicitly teach wherein resolution of the liquid level sensor assembly is in a range of about 1% to about 20% (interpreted consistent with MPEP 2173.05(b) as a term of degree)
Blanchard, provides that the length of each electrode segment of the sensor affects the tank resolution (col 2 lines 36-50), and also teaches that at the extremities of tanks, the resolution affects the ultimate accuracy of measurement (col 1 lines 34-55), and that there is a need to account for the complexity of the sensor/meter.
MPEP 2144.05 provides that optimization within prior art conditions or through routine experimentation is obvious to a person of ordinary skill in the art. In the instant case, the applicant did not demonstrate the criticality of the claimed range of about 1% to about 20% (and provided for multiple possible ranges with no demonstration of a special effect with respect to any particular range. Blanchard, as noted above, demonstrates that the resolution of the liquid level sensor assembly affects the accuracy of measurements, with a need to account for the complexity of the sensor/meter.
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho such that resolution of the liquid level sensor assembly is in a range of about 1% to about 20%, using the teachings of Blanchard, which provides for the result effective nature of the resolution, in that it affects the accuracy of measurements, with a need to account for the complexity of the sensor/meter.
With respect to claim 19, Cho, as modified, teaches the limitations of claim 17 above, however does not explicitly teach wherein resolution of the liquid level sensor assembly is finer than about 5% (interpreted consistent with MPEP 2173.05(b) as a term of degree, i.e. less than about 5%)
Blanchard, provides that the length of each electrode segment of the sensor affects the tank resolution (col 2 lines 36-50), and also teaches that at the extremities of tanks, the resolution affects the ultimate accuracy of measurement (col 1 lines 34-55), and that there is a need to account for the complexity of the sensor/meter.
MPEP 2144.05 provides that optimization within prior art conditions or through routine experimentation is obvious to a person of ordinary skill in the art. In the instant case, the applicant did not demonstrate the criticality of the claimed range of finer than about 5% and provided for multiple possible ranges with no demonstration of a special effect with respect to any particular range. Blanchard, as noted above, demonstrates that the resolution of the liquid level sensor assembly affects the accuracy of measurements, with a need to account for the complexity of the sensor/meter.
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho such that resolution of the liquid level sensor assembly is in a range of about finer than about 5% using the teachings of Blanchard, which provides for the result effective nature of the resolution, in that it affects the accuracy of measurements, with a need to account for the complexity of the sensor/meter.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho (KR 101122536 B1), Blanchard (US 3797311 A) and Hiraoka (US 6457852 B1), and further in view of Kim (US 20050198912 A1).
With respect to claim 16, Cho, as modifed, teaches the limitations of claim 15 above, however does not explicitly teach wherein the controller is operable to: generate second polishing slurry by aging a mixture of the polishing slurry and the fresh polishing slurry in the third tank; and supply the second polishing slurry to the polishing apparatus via the third tank. Cho, as noted in the rejection of claim 15 above, teaches a third tank operable to supply the polishing slurry to a polishing apparatus (41, fig. 1; [0077-0078], supplies slurry to CMP machine as in [0082]);
Kim, in the same field of endeavor, related to polishing provides for aging the polishing slurry before use ([0025]). Kim provides that this stabilizes the slurry before use ([0041]).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho with the teachings of Kim, and have the controller configured to generate second polishing slurry by aging a mixture of the polishing slurry and the fresh polishing slurry in the third tank; and supply the second polishing slurry to the polishing apparatus via the third tank, to provide a stable polishing slurry before dispense from the third tank.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho (KR 101122536 B1), Blanchard (US 3797311 A) and Hiraoka (US 6457852 B1), and further in view of Deshpande (US 20220193858 A1).
With respect to claim 20, Cho, as modified, teaches the limitations of claim 15 above, however does not explicitly teach wherein the controller is operable to: receive the second refill line generated by a trained machine learning model.
Hiraoka discloses that the refill line [or second refill line in a second polishing cycle] is determined based on different stored parameters (for the refill line, the level also takes into account the flow rate and processing information as in col 10 lines 49-65 and col 11 lines 10-21).
Deshpande, in the same field of endeavor, related to polishing, teaches of applying machine learning to improve polishing parameters by analyzing results with previous time series data captured for the result of changing one or more polishing parameters ([0011]), including of polishing fluid ([0087-0088]). Deshpande teaches that this improves upon existing models which were developed based on trial and error, and helps with control of a complex system with complicated relationships ([0007]) to provide for improved results ([0008])
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Cho with the teachings of Deshpande, and have had the controller receive the second refill line via a trained machine learning model, for improved results with a complex system.
Allowable Subject Matter
Claims 5-6 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With respect to claims 5-6, the claim language requires that the forecasted slurry consumption value includes determining the forecasted slurry consumption value based on a conductivity/rate of increase of the polishing slurry contained in the supply tank. In general, the prior art discusses the conductivity of the slurry in terms of slurry property See Seo (US 20210130651 A1) where the conductivity is described as a slurry property ([0025]). In addition, the prior art also demonstrates that the conductivity increases as the polishing time increases. See Kim (KR 20200115854 A), fig. 3, which shows “the electrical conductivity of the slurry 43 according to the operating time of the polishing module 40 of FIG. 1, […] the electrical conductivity of the slurry 43 and the deionized water 53 gradually increases within the deionized water discharge time ET”. Matsuo, cited above, also demonstrates the relationship between conductivity and the freshness of the slurry. However, the prior art does not relate a forecasted slurry consumption to a conductivity of a slurry contained in the supply tank. At most, if the slurry is recycled (recirculation of slurry is taught by Byers along with a conductivity sensor), the conductivity of any slurry left in the supply tank is indicative of how spent/not fresh the slurry is based on past polishing cycles or consumption. The prior art also provides that past results can be indicative of trends in future results when analyzed, for example using the teachings of Deshpande. In addition, the method as recited in claim 1 requires that the tank contain slurry that is mixed with fresh slurry. After consideration of the prior art, the examiner came to a conclusion that the limitations required by claims 5-6 are non-obvious in that it relates two parameters “a forecasted slurry consumption value” and “a conductivity/rate of increase of the polishing slurry contained in the supply tank” that have not been demonstrated in the prior art to have more than a tenuous relationship.
Conclusion
The examiner notes that, it may be helpful, to provide for a more defined relationship between the input/result of various determinations that are claimed, beyond language that recites “based’ or “associated” which are otherwise broad terms not otherwise indicative of how specifically the input is related or used to determine the result, and to relate the various parameters that the instant machine learning model in a more specific or concrete way, as currently the parameters are generically used to train a model without specific weights or relationships, and the examiner finds that such generic use of various data points, demonstrated to be used during polishing, to be a obvious use/application of generic machine learning to identify possible trends or relationships (i.e. various polishing history data relating inputs/outputs -> non-specific machine learning model -> output(s)).
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/Steven Huang/Examiner, Art Unit 3723
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