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 .
Response to Arguments
Applicant’s arguments with respect to claims as amended have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 19, 21, 24, 25, 27, 28, 30, 31 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication 2018/0082826 to Guha et al. in view of US Publication 2021/0116896 to Arabshahi et al.
Regarding Claim 19, Guha et al. teaches a computer-implemented method for
controlling a wafer production process in real-time (Paragraphs 44, 81 102, 105, 122) using a trained machine learning, ML, model (150, 180), the method comprising:
receiving sensor data from a plurality of sensors (136) monitoring the wafer production
process in real-time; inputting the sensor data from the plurality of sensors into a neural
network (Paragraphs 83-85) of the trained ML model (150, 180); generating, using the
trained ML model (150, 180), a latent representation of a state of a plasma used in the wafer production process (processing state of the plasma reactor); and adjusting in real-time, using the generated latent representation, at least one control parameter
(compensation processing and turning knobs) of a plasma reactor used in the wafer production process
Regarding Claim 19, Guha et al. teaches the method of the invention substantially as claimed, but does not expressly teach combining sensor data having different temporal dimensionality. However, Arabshahi et al. teaches (Paragraph 53) combining sensor data having different temporal dimensionality in a semiconductor plasma processing environment. It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to combine sensor data having different temporal dimensionality in the method of Guha et al. in order to incorporate the changing trajectory of sensor values in real time with predictable results.
Regarding Claim 21, Guha et al. teaches receiving sensor data comprises
receiving at least one of: RF power applied to the plasma reactor, temperature of
chamber furniture inside the plasma reactor, pressure inside the plasma reactor, gas flow rate into the plasma reactor, plasma impedance, and plasma electron density (See at least Paragraph 50 and Table A).
Regarding Claim 24, Guha et al. teaches comparing the generated latent representation of the state of the plasma with a desired latent representation of an ideal
state of the plasma; and identifying any difference (See at least Paragraphs 16 and 99)
between the generated and desired latent representations.
Regarding Claim 25, Guha et al. teaches adjusting at least one control parameter
of a plasma reactor used in the wafer production process comprises: determining at
least one parameter of the wafer production process to adjust to minimize any identified
difference between the generated latent representation and the desired latent
representation; and adjusting (Paragraph 52) the determined at least one parameter.
Regarding Claim 27, Guha et al. teaches combining the sensor data comprises
combining sensor data having different spatial and/or temporal dimensionality
(Paragraph 42).
Regarding Claim 28, Guha et al. teaches a computer-implemented method for
training a machine learning, ML model (150, 180) for controlling a wafer production
process in real-time (Paragraphs 44, 81 102, 105, 122), the method comprising:
receiving training data (processing data and stored data offline; See at least Paragraph
82) comprising sensor data from a plurality of sensors (136) monitoring a wafer
production process; inputting the training data into a neural network (Paragraphs 83-85)
of the ML model (150, 180); and training the neural network of the ML model (150, 180)
to generate a latent representation of a state of a plasma (processing state) in a plasma
reactor used in the wafer production process.
Regarding Claim 19, Guha et al. teaches the method of the invention substantially as claimed, but does not expressly teach combining sensor data having different temporal dimensionality. However, Arabshahi et al. teaches (Paragraph 53) combining sensor data having different temporal dimensionality in a semiconductor plasma processing environment. It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to combine sensor data having different temporal dimensionality in the method of Guha et al. in order to incorporate the changing trajectory of sensor values in real time with predictable results.
Regarding Claim 30, Guha et al. teaches (See at least Paragraph 50 and Table
A) each set of data items further comprises at least one of: RF power applied to the
plasma reactor, temperature inside the plasma reactor, pressure inside the plasma
reactor, gas flow rate into the plasma reactor, plasma impedance, and plasma electron density.
Regarding Claim 31, Guha et al. teaches training the neural network comprises
training an encoder of the neural network to: combine each set of data items to
generate a latent representation of the state of the plasma at a particular point in time.
Regarding Claim 33, Guha et al. teaches training the neural network further
comprises: inputting, into the neural network, a desired latent representation of an ideal state of the plasma; training the neural network to identify any difference (compensation vector) between each generated latent representation and the desired latent representation (See at least Paragraphs 72 and 99); and determining at least one
parameter (compensation value) of the wafer production process to adjust to minimize
any identified difference between each generated latent representation and the desired
latent representation.
Claims 20 and 29 are rejected under 35 U.S.C. 103 as being unpatentable
over US Publication 2018/0082826 to Guha et al. in view of US Publication 2021/0116896 to Arabshahi et al. and in view of US Publication 2007/0224709 to Ogasawara et al.
Regarding Claims 20 and 29, Guha et al. teaches receiving sensor data
comprises at least one optical emission spectrograph of the plasma (Paragraphs 50, 66,
74) and additional sensors may be used (Paragraph 51), but does not expressly teach
an image of the plasma us used as sensor data. However, the use of image sensor data
is well known for plasma process control. For example, Ogasawara et al. teaches
(Paragraphs 57 and 58) monitor (50) camera capturing an image of a plasma may be
used as a process controller (60) input regarding the state of a plasma sheath. It would
have been obvious to one of ordinary skill in the art at the time of the claimed invention
to provide at least one image of the plasma as taught by Osasawara et al. as a data
sensor input in the method of Guha et al. in order to provide plasma sheath data and
process control with predictable results.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over US
Publication 2018/0082826 to Guha et al. in view of US Publication 2021/0116896 to Arabshahi et al. and in view of US Publication 2020/166909 to Noone et al.
Regarding Claim 23, Guha et al. does not expressly teach the neural network
comprises an autoencoder. However, an autoencoder is a well-known algorithm for data
set analysis in a machine learning process including neural network (Noone et al.
Paragraphs 9 and 225) It would have been obvious to one of ordinary skill in the art at
the time of the claimed invention to provide an autoencoder as taught by Noone et al. in
the method of Guha et al. in order to provide machine learning process control with
predictable results.
Claims 26 is rejected under 35 U.S.C. 103 as being unpatentable over US
Publication 2018/0082826 to Guha et al. in view of US Publication 2021/0116896 to Arabshahi et al. and in view of US Publication 2020/0111650 to Oka.
Regarding Claim 26, Guha et al. does not expressly teach outputting an alert to
an operator of the plasma reactor when the identified difference between the generated
and latent representations exceeds a threshold value or cannot be minimized by
adjusting at least one parameter. However, use of an operator alarm is well known in
plasma process control operations. For example, Oka teaches an alarm unit for issuing
an alarm when a predetermined degree of change is detected in a plasma processing
condition (Paragraph 160) It would have been obvious to one of ordinary skill in the art
at the time of the claimed invention to provide an alarm as taught by Oka in the method
of Guha et al. in order to notify an operator in the well-known manner.
Claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over US
Publication 2018/0082826 to Guha et al. in view of US Publication 2021/0116896 to Arabshahi et al. and in view of US Patent 6,351,683 to Johnson et al.
Regarding Claim 32, Guha et al. teaches training the neural network further
comprises training a decoder (implicit) of the neural network to: reconstruct, from the
generated latent representation, a set of data items corresponding to the generated
latent representation, but does not expressly teach a step to minimize, using
backpropagation, a difference between the set of data items and the reconstructed set
of data items. However, backpropagation is a well-known algorithm for neural network
training. For example, Johnson et al. teaches backpropagation algorithm for controlling
a plasma process (Col. 14, Lines 59-67) by training a neural network. It would have
been obvious to one of ordinary skill in the art at the time of the claimed invention to
provide a backpropagation algorithm as taught by Johnson et al. in the method of
Guha et al. in order to provide neural network training in a plasma control process with
predictable results.
Conclusion
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.
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/ROBERTS P CULBERT/Primary Examiner, Art Unit 1716