Part, sensor, and metrology data integration

Patent metadata

Field	Value
Publication number	US-2022260978-A1
Application number	US-202117177978-A
Country	US
Kind code	A1
Filing date	Feb 17, 2021
Priority date	Feb 17, 2021
Publication date	Aug 18, 2022
Grant date	—

Abstract

A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

First claim

What is claimed is:

1 . A method comprising:
receiving a plurality of sets of part data, each of the plurality of sets of part data being associated with a corresponding part of substrate processing equipment; receiving a plurality of sets of sensor data, each of the plurality of sets of sensor data being associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates; receiving a plurality of sets of metrology data, each of the plurality of sets of metrology data being associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that comprises the corresponding part; and generating a plurality of sets of aggregated part-sensor-metrology data, each of the plurality of sets of aggregated part-sensor-metrology data comprising a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data; and causing analysis of the plurality of sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

2 . The method of claim 1 , wherein the plurality of sets of part data comprise one or more of part manufacturing data, part measurement data, or part material property data.

3 . The method of claim 1 , wherein at least a portion of the plurality of sets of part data are measured via one or more of automated optical inspection (AOI) equipment, an atomic force microscope (AFM), or a coordinate measurement (CMM) machine.

4 . The method of claim 1 , wherein the plurality of sets of sensor data comprise one or more of pressure data, temperature data, time data, or gas flow data.

5 . The method of claim 1 , wherein the plurality of sets of metrology data comprise one or more of on-wafer metrology data, imaging data, or thickness data.

6 . The method of claim 1 , wherein:
each of the plurality of sets of part data comprises corresponding part values associated with producing the corresponding part and a corresponding part identifier; each of the plurality of sets of sensor data comprises corresponding sensor values associated with producing the one or more corresponding substrates by the substrate processing equipment and a corresponding sensor data identifier; and each of the plurality of sets of metrology data comprises corresponding metrology values associated with the one or more corresponding substrates produced by the substrate processing equipment and a corresponding metrology data identifier.

7 . The method of claim 6 , wherein the generating of the plurality of sets of aggregated part-sensor-metrology data comprises:
determining common portions between each corresponding part identifier, each corresponding sensor data identifier, and each corresponding metrology data identifier to identify part-sensor-metrology matches; and for each of the part-sensor-metrology matches, generating a corresponding set of aggregated part-sensor-metrology data that comprises a respective set of part data that corresponds to the corresponding part identifier, a respective set of sensor data that corresponds to the corresponding sensor data identifier, and a respective set of metrology data that corresponds to the corresponding metrology data identifier to generate the plurality of sets of aggregated part-sensor-metrology data.

8 . The method of claim 1 , wherein the causing of the analysis of the plurality of sets of aggregated part-sensor-metrology data comprises providing the plurality of sets of aggregated part-sensor-metrology data to train a machine learning model.

9 . The method of claim 1 , wherein the causing of the analysis of the plurality of sets of aggregated part-sensor-metrology data comprises providing the plurality of sets of aggregated part-sensor-metrology data to a trained machine learning model and receiving, from the trained machine learning model, the one or more outputs.

10 . The method of claim 1 , wherein the causing of the analysis of the plurality of sets of aggregated part-sensor-metrology data comprises storing the plurality of sets of aggregated part-sensor-metrology data to train a machine learning model to provide a trained machine learning model, and wherein the trained machine learning model is capable of generating the one or more outputs to perform the corrective action.

11 . The method of claim 1 , wherein performing the corrective action comprises one or more of:
updating design of the corresponding part; updating quality of the corresponding part; updating dimensions of the corresponding part; updating feature layout of the corresponding part; updating part manufacturing operations to produce the corresponding part; or performing root cause analysis to determine updates to the corresponding part or to the one or more corresponding substrate processing operations.

12 . A system comprising:
a memory; and a processing device, coupled to the memory, to:
receive a plurality of sets of part data, each of the plurality of sets of part data being associated with a corresponding part of substrate processing equipment;
receive a plurality of sets of sensor data, each of the plurality of sets of sensor data being associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates;
receive a plurality of sets of metrology data, each of the plurality of sets of metrology data being associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that comprises the corresponding part; and
generate a plurality of sets of aggregated part-sensor-metrology data, each of the plurality of sets of aggregated part-sensor-metrology data comprising a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data; and
cause analysis of the plurality of sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

13 . The system of claim 12 , wherein:
the plurality of sets of part data comprise one or more of part manufacturing data, part measurement data, or part material property data; and at least a portion of the plurality of sets of part data are measured via one or more of automated optical inspection (AOI) equipment, an atomic force microscope (AFM), or a coordinate measurement (CMM) machine.

14 . The system of claim 12 , wherein to cause the analysis of the plurality of sets of aggregated part-sensor-metrology data, the processing device is to provide the plurality of sets of aggregated part-sensor-metrology data to train a machine learning model.

15 . The system of claim 12 , wherein to cause the analysis of the plurality of sets of aggregated part-sensor-metrology data, the processing device is to provide the plurality of sets of aggregated part-sensor-metrology data to a trained machine learning model and receiving, from the trained machine learning model, the one or more outputs.

16 . The system of claim 12 , wherein to cause the analysis of the plurality of sets of aggregated part-sensor-metrology data, the processing device is to store the plurality of sets of aggregated part

Assignees

• Applied Materials Inc

Inventors

• Sin Garrett Ho-Yee
• Bhatia Sidharth
• Guyomard Katty Marie Lydia Gamon
• Jafari Shawyon
• Pai Heng-Cheng
• Nambiar Pramod
• Brillhart Paul Lukas
• Durukan Ilker

Classifications

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G05B13/0205

  not using a model or a simulator of the controlled system · CPC title

• G05B2219/45031

  Manufacturing semiconductor wafers · CPC title

• G01N33/00 Primary

  Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00 · CPC title

• G06F2119/22

  Yield analysis or yield optimisation · CPC title