# US 12,153,411 B2 · Predictive process control for a manufacturing process

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- Number: US 12,153,411 B2
- Document ID: US12153411B2
- Status: Granted
- Kind: B2
- Application number: 18/329,265
- Filed: 2023-06-05
- Published: 2023-10-05
- Granted: 2024-11-26
- Earliest claimed: 2019-06-24
- Assignees: Nanotronics Imaging, Inc.
- Applicants: Nanotronics Imaging, Inc.
- Inventors: Matthew C. Putman; John B. Putman; Vadim Pinskiy; Damas Limoge
- Web page: https://patentgrep.com/patent/US12153411B2

## CPC

- G05B 19/41875: {characterised by quality surveillance of production}
- G05B 13/027: {using neural networks only}
- G05B 2219/32193: Ann, neural base quality management
- G05B 2219/32194: Quality prediction
- G05B 2219/32195: Feedforward quality control
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)

## Abstract

Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.

## Claims

### Claim 1

A computer-implemented method comprising:
receiving a first set of in-process inputs from a first processing station, at a deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process;
identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process;
predicting, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs;
determining, by the deep learning controller, that the expected value for the article of manufacture is not in-specification;
determining, by the deep learning controller, a plurality of key influencers and parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture; and
based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station, the downstream parameters corresponding to the plurality of key influencers, wherein the adjusting causes the final quality metric to be in-specification.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, further comprising:
adjusting, by the deep learning controller, further downstream parameters of a further downstream processing station in accordance with the plurality of key influencers.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, further comprising:
generating, by the deep learning controller, one or more control inputs, wherein the deep learning controller adjusts the downstream parameters of the downstream processing station in accordance with the one or more control inputs.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, wherein adjusting, by the deep learning controller, the downstream parameters of the downstream processing station comprises:
identifying a control input of the one or more control inputs that corresponds to a key influencer of the plurality of key influencers; and
adjusting the downstream parameters based on the control input corresponding to the key influencer.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein the plurality of key influencers is associated with a plurality of parameters that affect characteristics of outputs from the first processing station.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein determining, by the deep learning controller, the plurality of key influencers and the parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture comprises:
scoring a plurality of parameters in outputs generated by the first processing station; and
identifying a subset of the plurality of parameters that exceed a threshold score.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein determining, by the deep learning controller, the plurality of key influencers and the parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture comprises:
identifying a plurality of parameters in outputs generated by the first processing station; and
determining a subset of the plurality of parameters that exceed a threshold level of influence on the outputs generated by the first processing station.

### Claim 8

A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by a processor, causes a computing system to perform operations comprising:
receiving a first set of in-process inputs from a first processing station, at a deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process;
identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process;
predicting, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs;
determining, by the deep learning controller, that the expected value for the article of manufacture is not in-specification;
determining, by the deep learning controller, a plurality of key influencers and parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture; and
based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station, the downstream parameters corresponding to the plurality of key influencers, wherein the adjusting causes the final quality metric to be in-specification.

### Claim 9 (depends on 8)

The non-transitory computer readable medium of claim 8, further comprising:
adjusting, by the deep learning controller, further downstream parameters of a further downstream processing station in accordance with the plurality of key influencers.

### Claim 10 (depends on 8)

The non-transitory computer readable medium of claim 8, further comprising:
generating, by the deep learning controller, one or more control inputs, wherein the deep learning controller adjusts the downstream parameters of the downstream processing station in accordance with the one or more control inputs.

### Claim 11 (depends on 10)

The non-transitory computer readable medium of claim 10, wherein adjusting, by the deep learning controller, the downstream parameters of the downstream processing station comprises:
identifying a control input of the one or more control inputs that corresponds to a key influencer of the plurality of key influencers; and
adjusting the downstream parameters based on the control input corresponding to the key influencer.

### Claim 12 (depends on 8)

The non-transitory computer readable medium of claim 8, wherein the plurality of key influencers is associated with a plurality of parameters that affect characteristics of outputs from the first processing station.

### Claim 13 (depends on 8)

The non-transitory computer readable medium of claim 8, wherein determining, by the deep learning controller, the plurality of key influencers and the parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture comprises:
scoring a plurality of parameters in outputs generated by the first processing station; and
identifying a subset of the plurality of parameters that exceed a threshold score.

### Claim 14 (depends on 8)

The non-transitory computer readable medium of claim 8, wherein determining, by the deep learning controller, the plurality of key influencers and the parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture comprises:
identifying a plurality of parameters in outputs generated by the first processing station; and
determining a subset of the plurality of parameters that exceed a threshold level of influence on the outputs generated by the first processing station.

### Claim 15

A system comprising:
a processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:
receiving a first set of in-process inputs from a first processing station, at a deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process;
identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process;
predicting, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs;
determining, by the deep learning controller, that the expected value for the article of manufacture is not in-specification;
determining, by the deep learning controller, a plurality of key influencers and parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture; and
based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station, the downstream parameters corresponding to the plurality of key influencers, wherein the adjusting causes the final quality metric to be in-specification.

### Claim 16 (depends on 15)

The system of claim 15, wherein the operations further comprise:
generating, by the deep learning controller, one or more control inputs, wherein the deep learning controller adjusts the downstream parameters of the downstream processing station in accordance with the one or more control inputs.

### Claim 17 (depends on 16)

The system of claim 16, wherein adjusting, by the deep learning controller, the downstream parameters of the downstream processing station comprises:
identifying a control input of the one or more control inputs that corresponds to a key influencer of the plurality of key influencers; and
adjusting the downstream parameters based on the control input corresponding to the key influencer.

### Claim 18 (depends on 15)

The system of claim 15, wherein the plurality of key influencers is associated with a plurality of parameters that affect characteristics of outputs from the first processing station.

### Claim 19 (depends on 15)

The system of claim 15, wherein determining, by the deep learning controller, the plurality of key influencers and the parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture comprises:
scoring a plurality of parameters in outputs generated by the first processing station; and
identifying a subset of the plurality of parameters that exceed a threshold score.

### Claim 20 (depends on 15)

The system of claim 15, wherein determining, by the deep learning controller, the plurality of key influencers and the parameters associated with the plurality of key influencers that impact the expected value for the article of manufacture comprises:
identifying a plurality of parameters in outputs generated by the first processing station; and
determining a subset of the plurality of parameters that exceed a threshold level of influence on the outputs generated by the first processing station.