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 .
Election/Restrictions
Applicant's election with traverse of Group I, claims 1-7 in the reply filed on 06/01/2026 is acknowledged. The traversal is on the ground(s) that examining all of the claims does not present undue searching . This is not found persuasive because although the claims have come common subject matter the inventions are related to different aspects of the technology. Claims 1-7 is directed to a recipe optimization method and concerns process modeling, recipe optimization and generation of a prediction model. In contrast, claim 8, is directed to a heat treatment apparatus having a container, gas supply, temperature regulating furnace, and controller. Claim 8 requires searching art directed to the structural and functional arrangement of a heat treating system.
The requirement is still deemed proper and is therefore made FINAL.
Claim Objections
Claim 3 is objected to because of the following informalities: The claim recites the phrases “a parameters” and “as an explanatory variables” which are grammatically inconsistent. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: “wherein” should replace “in” in line 1. Appropriate correction is required.
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 3-4, 6 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.
As to claim 3, the metes and bounds of the limitation “the process model serving as a target variable is generated by inputting a parameters of the preliminary film formation process as an explanatory variables along with the predicted model’ are unclear. It is unclear whether the recited “target variable” is the process model itself, a parameter defining the process model, or an output generated by the prediction model. It is further unclear whether a single parameter or multiple parameters of the preliminary film formation process are required in view of the inconsistent recitations “a parameters” and “an explanatory variables.”
NOTE: For prior art purposes, the examiner will interpret the claim to require preliminary process parameters are explanatory variables input into the prediction model to obtain the parameter/process model.
Claim 6 recites the limitation "other apparatuses" in line 3. There is insufficient antecedent basis for this limitation in the claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 2006/0122728) in view of Holmqvist et al. “A model-based methodology for the analysis and design of atomic layer deposition processes—Part II: Experimental validation and mechanistic analysis and Kasai (US 2017/0278699).
As to claim 1, Chang et al. discloses a model-based temperature control for forming a film in a semiconductor batch furnace. Chang teaches a process comprising providing a slope coefficient matrix represents a relationship between temperature and film thickness (see 0008, 0017). The slope coefficient matrix is experimentally determined using previous process runs (see 0019). Chang further defines a first heating model (initial recipe), performs a first process run according to the first heating model, measures the resulting film thickness, and performs statistical control analysis using the measured thickness and slope coefficient matrix. Based on analysis, Chang determines corrected temperature settings and modifies the first heating model to obtain a second heating model (updated recipe) (see 0019-26).
Chang et al. fails to expressly disclose forming from past evaluation data, a prediction model that predicts or generates a process model representing a relationship between tan amount of temperature change and an amount of film thickness change as required by claim 1.
Holmqvist et al. teaches model-based analysis and optimization of an atomic layer depositing process where experimentally measured film thickness data is used to identify parameters of a physical process model for film growth. Holmqvist forms a non-linear parameter estimation problem for identifying kinetic parameters of an ALD gas surface reaction mechanism from film thickness measurements. The parameter estimates are obtained using a least-squares method and uses Arrhenius representation of the reaction kinetics and evaluates the reliability of the estimated parameters. The resulting calibrated process model predicts film thickness profiles over different process operating conditions.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to apply Holmqvist’s known kinetic parameter estimation technique to the historical temperature and film-thickness data used by Chang. One would have been motivated to do so since Chang recognize that prior temperature/thickness process data should be analyzed by regression to characterize film formation sensitivity to temperate, while Holmqvist teaches that measured film-thickness data can be used to estimate the kinetic parameters defining a physical deposition model. Applying Holmqvist ‘s parameter-estimation technique to Chang’s historical process data would therefore have provided a known means for determining parameters of a physical process model representing temperature-dependent film growth behavior.
Kasai further teaches applying a process model in a semiconductor film formation process to optimize film formation conditions based on actual process conditions and measured film characteristics. Kasai teaches a substrate processing system that performs a film formation process and stores a process model representing the influence of process conditions on film characteristics (see abstract, Fig. 3, 0069-0075). Kasai teaches a temperature film thickness model representing the influence of wafer temperate on film thickness and uses the process model, actual film formation conditions, and measure film characteristics to determine film formation conditions obtaining a desired film characters.
It would have been obvious to one having ordinary sill in the art before the effective filing date of the claimed invention to modify the process of Chang et al. modified by Holmqvist to include the process-model-based optimization procedure of Kasai. One would have been motivated to do so because they both address determining semiconductor film formation conditions that produced desired film results. Kasai further teaches using a temperature-film thickness process model, actual process conditions, and measured film characteristics to calculate film formation conditions satisfying a desired target. The combination would therefore have provided a predicable means for improving the representation and predictive capability of the temperature /film thickness relationship for more efficiently determine film-formation conditions producing a target thickness.
As to claim 2, Holmqvist further teaches generating a physical film formation model based on parameters determined from experimentally measured film-thickness data, while Kasi teaches optimizing film formation conditions using a process model representing the influence of process conditions on resulting film characteristics. The combination of Chang, Holmqvist, and Kasai teaches generating the process model based on the data driven parameter estimation methodology and optimizing the recipe using the resulting model.
As to 3, Holmqvist teaches using operating conditions and parameter estimation of the physical model. The parameter estimation method relates process operating conditions to kinetic parameters defining the resulting film growth model.
As to claim 4, Kasai teaches that the film formation conditions used include process parameters such as substrate temperature, processing gas, gas flow, pressure, and hardware information. (see 0015, 0066).
As to claim 5, Kasai further teaches repeatedly performing the film formation and recipe optimization procedure based on if the measured film thickness is within an allowable range of the target thickness (see 0072-0074). Kasai teaches that when the measured film thickness is within the allowable range, the adjustment process is terminated, when the thickness is outside of the range recipe optimization is performed using measured film thickness, the temperature film thickness model, and actual measured process conditions (see 0075). Kasai does not expressly disclose adding the out-of-range preliminary film formation results to the historical evaluation data and recreating the predication model followed by generating the process model mased on the recreated prediction model as required.
Chang teaches that newly obtained film thickness measurements from successive process runs are used to update model/control relationships used in determining subsequent processing conditions. It would have been obvious to incorporate the newly obtained out of range film thickness result from Kasai’s preliminary process into the evaluation data used for model estimation and to recalculate the model parameters before the next optimization iteration to improve the accuracy of the model used to calculate the new recipe.
As to claim 7, Chang teaches the film formation process comprises supplying a process gas to a plurality of substrate in a vertical position inside a furnace and controlling the temperature using heating elements (see 0017).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 2006/0122728) in view of Holmqvist et al. “A model-based methodology for the analysis and design of atomic layer deposition processes—Part II: Experimental validation and mechanistic analysis and Kasai (US 2017/0278699) as applied to claim 1 are as stated above, and further in view of Park “Multitask learning for virtual metrology in semiconductor manufacturing systems.”
The teachings of Chang, Holmqvist et al. and Kasai as applied to claim 1 are as stated above.
Chang, Holmqvist et al. and Kasai fail to teach the prediction model is calculated by regression using past data from film formation process performed in other apparatuses as required by claim 6.
Park teaches generating predictive models for semiconductor manufacturing using process and metrology data obtaining from multiple process chambers. Park recognizes that the number of measured wafers available from an individual chamber may be insufficient to form a reliable prediction model and teaches using information obtained from multiple chambers to increase the amount of information available for predictive modeling. Park teaches the use of regression-based modeling techniques (see abstract, Introduction).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the process of Chang modified by Holmqvist and Kasai to calculate prediction model using past data obtained from processes performed in other apparatuses as taught by Park et al. One would have been motivated to do so because Park recognizes that individual process chambers may be insufficient to form a reliable prediction model and teaches that using data form multiple related chambers increase the number of observations available to the model and improved predictive performance.
Conclusion
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/CACHET I. PROCTOR/
Examiner
Art Unit 1712
/CACHET I PROCTOR/ Primary Examiner, Art Unit 1712
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