Method and apparatus with operational control dependent on multiple processes

Patent metadata

Field	Value
Publication number	US-2024210925-A1
Application number	US-202318530692-A
Country	US
Kind code	A1
Filing date	Dec 6, 2023
Priority date	Dec 22, 2022
Publication date	Jun 27, 2024
Grant date	—

Abstract

A method including generating sequence data based on measured values of respective one or more process factors of each of a plurality of processes of a semiconductor fabrication process for a wafer within the semiconductor fabrication process, generating a temporary quality index of the wafer using a second neural network connected to a first neural network that is provided the sequence data, training the first neural network and the second neural network based on a loss between the temporary quality index and a set actual quality index of the wafer, selecting a control process from among the plurality of processes using at least one of the trained first neural network and/or the trained second neural network, and selecting a control factor from among multiple process factors of the selected process using the trained second neural network.

First claim

What is claimed is:

1 . A processor-implemented method, the method comprising:
generating sequence data based on measured values of respective one or more process factors of each of a plurality of processes of a semiconductor fabrication process for a wafer within the semiconductor fabrication process; generating a temporary quality index of the wafer using a second neural network connected to a first neural network that is provided the sequence data; training the first neural network and the second neural network based on a loss between the temporary quality index and a set actual quality index of the wafer; selecting a control process from among the plurality of processes using at least one of the trained first neural network and/or the trained second neural network; and selecting a control factor from among multiple process factors of the selected process using the trained second neural network.

2 . The method of claim 1 , wherein the first neural network is trained to infer process factors, from among all the process factors belonging to the plurality of processes, that affect quality more than other control factors of the all control factors.

3 . The method of claim 1 , wherein the second neural network comprises:
a transformer encoder; and a multi-layer perceptron (MLP).

4 . The method of claim 3 , wherein the generating of the temporary quality index comprises:
correcting the generated sequence data in response to inputting the sequence data to the first neural network; generating, by the second neural network, a plurality of embedding vectors by performing linear projection on each of the corrected sequence data to be respective vectors of the plurality of embedding vectors having a preset size; and generating, by the second neural network, the temporary quality index of the wafer using the MLP provided an output vector of the transformer encoder that is provided the plurality of embedding vectors.

5 . The method of claim 4 , wherein the transformer encoder performs:
positional encoding on each of the plurality of embedding vectors; and provides embedding vectors obtained through the positional encoding to the transformer encoder.

6 . The method of claim 3 , wherein the transformer encoder comprises:
a plurality of encoders, each encoder comprising a multi-head attention layer configured to perform self-attention at least once, the plurality of encoders being connected in series.

7 . The method of claim 1 , wherein the training comprises:
performing the training of the first neural network and the second neural network together; and retraining the second neural network using the trained first neural network.

8 . The method of claim 1 , wherein the selecting of the control process comprises:
selecting a preset number of processes as the control process from among the plurality of processes, in a descending order from a corresponding process that has a greatest number of respective process factors selected by the trained first neural network.

9 . The method of claim 1 , wherein the selecting of the control process comprises, for each of the plurality of processes:
calculating a respective score indicating an interaction between a corresponding process and other processes for each of plural wafers using a transformer encoder of the trained second neural network; calculating an average of the respective scores as a respective final score of the corresponding process; and selecting a preset number of select processes as the control process from among the plurality of processes, in a descending order from a process having a highest respective final score.

10 . The method of claim 1 , wherein the selecting of the control process comprises:
for each of some process factors selected by the trained first neural network from among all the process factors of the plurality of processes:
calculating a Shapley value of a corresponding process factor for each of plural wafers using a transformer encoder of the trained second neural network;
calculating an average of absolute values of the Shapley values calculated for plural wafers as a respective final score of the corresponding process factor; and
selecting a preset number of select process factors as the control factor from among the some process factors in a descending order according to the respective final score of the select process factors from a process factor having a highest respective final score.

11 . An electronic device, the device comprising:
a processor configured to:
generate sequence data based on measured values of respective one or more process factors of each of a plurality of processes of a semiconductor fabrication process for a wafer within the semiconductor fabrication process;
generate a temporary quality index of the wafer using a second neural network connected to a first neural network that is provided the sequence data;
train the first neural network and the second neural network based on a loss between the temporary quality index and a set actual quality index of the wafer;
select a control process from among the plurality of processes using at least one of the trained first neural network or the trained second neural network; and
select a control factor from among multiple process factors of the selected process using the trained second neural network.

12 . The device of claim 11 , wherein the first neural network is trained to infer process factors, from among all the process factors belonging to the plurality of processes, that affect quality more than other control factors of the all control factors.

13 . The device of claim 11 , wherein the second neural network comprises:
a transformer encoder; and a multi-layer perceptron (MLP).

14 . The device of claim 13 , wherein the generating of the temporary quality index comprises:
correcting the generated sequence data in response to inputting the sequence data to the first neural network; generating, by the second neural network, a plurality of embedding vectors by performing linear projection on each of the corrected sequence data to be respective vectors of the plurality of embedding vectors having a preset size; and generating, by the second neural network, the temporary quality index of the wafer using the MLP provided an output vector of the transformer encoder that is provided the plurality of embedding vectors.

15 . The electronic device of claim 14 , wherein the processor is further configured to:
perform positional encoding on each of the plurality of embedding vectors; and provide embedding vectors obtained through the positional encoding to the transformer encoder.

16 . The electronic device of claim 13 , wherein the transformer encoder comprises:
a plurality of encoders, each encoder comprising a multi-head attention layer configured to perform self-attention at least once, the plurality of encoders being connected in series.

17 . The electronic device of claim 11 , wherein the processor is further configured to:
train both the first neural network and the second neural network together; and retrain the second neural network using the trained first neural network.

18 . The electronic device of claim 11 , wherein the processor is further configured to:
select a preset number of processes as the control process from among the plurality of processes, in a descending order from a corresponding process that has a greatest number of respective process factors selected by the trained first neural network.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Ji Dong Jin
• You Kaeweon
• Lee Jiung
• Hong Seok Hyun

Classifications

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G05B23/0254

  based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks · CPC title

• G05B23/0275

  Fault isolation and identification, e.g. classify fault; estimate cause or root of failure · CPC title

• G05B23/0248 Primary

  Causal models, e.g. fault tree; digraphs; qualitative physics · CPC title

• G05B19/41875

  characterised by quality surveillance of production · CPC title