Method and device with semiconductor device quality prediction

Patent metadata

Field	Value
Publication number	US-2025245403-A1
Application number	US-202519042933-A
Country	US
Kind code	A1
Filing date	Jan 31, 2025
Priority date	Jan 31, 2024
Publication date	Jul 31, 2025
Grant date	—

Abstract

A method for predicting quality of a workpiece includes: obtaining optical emission spectroscopy (OES) raw data of the workpiece, the OES raw data having a time dimension and a wavelength dimension; selecting, in the wavelength dimension, a first portion of the OES raw data that falls within wavelength ranges selected based on a material used in a process of producing the workpiece; selecting, in the time dimension, a second port of the OES raw data that falls within a time range corresponding to a specific step of the process, where the specific step is to be analyzed; generating tokens by grouping the selected portions of the OES raw data; and training a model with the tokens.

First claim

What is claimed is:

1 . A method for predicting quality of a workpiece, the method comprising:
obtaining optical emission spectroscopy (OES) raw data of the workpiece, the OES raw data having a time dimension and a wavelength dimension; selecting, in the wavelength dimension, a first portion of the OES raw data that falls within wavelength ranges selected based on a material used in a process of producing the workpiece; selecting, in the time dimension, a second port of the OES raw data that falls within a time range corresponding to a specific step of the process, where the specific step is to be analyzed; generating tokens by grouping the selected portions of the OES raw data; and training a model with the tokens.

2 . The method of claim 1 , wherein
the selected wavelength ranges correspond to OES responsivity of the material.

3 . The method of claim 1 , wherein
the selecting of the first portion of the OES raw data further includes selecting OES raw data having OES intensity greater than a predetermined reference.

4 . The method of claim 1 , wherein
the generating of the tokens includes grouping the selected portions of the OES raw data by dividing the second portion of the OES data into time sections with a predetermined size.

5 . The method of claim 4 , wherein
the generating of the tokens includes grouping the OES raw data so that one token corresponds to one wavelength range of the first portion of the OES raw data and corresponds to one time section of the second portion of the OES raw data.

6 . The method of claim 4 , wherein
the generating of the tokens includes grouping the OES raw data so that one token corresponds to wavelength ranges of the first portion of the OES raw data and corresponds to one time section of the second portion of the OES raw data.

7 . The method of claim 1 , wherein
the time range is selected by determining whether a data amount belonging to a time range corresponding to the specific step of the process to be analyzed is greater than a predetermined threshold value in the OES raw data, and when it is determined that the data amount is greater than the threshold value, generating the second portion of the OES data by performing a sampling or a moving average in the time dimension of the OES raw data.

8 . The method of claim 6 , further comprising
obtaining a yield of the specific step to be analyzed, and determining whether the yield is higher than a predetermined reference value, wherein the generating of tokens includes when the yield is determined to be higher than a reference value, adding an extended region to a wavelength range corresponding to one token, additionally dividing the time section corresponding to the one token into a detailed time section, and grouping the OES raw data so that the one token corresponds to the wavelengths to which the extended region is added and the additionally divided time section.

9 . The method of claim 1 , wherein
the model includes a transformer model.

10 . The method of claim 1 , wherein
the specific step to be analyzed includes an etching process or a deposition process.

11 . A method for comprising:
obtaining optical emission spectroscopy (OES) raw data; receiving a first model trained by a first method and a second model provided by a second method differing from the first method; selecting a model to be used in quality prediction from among the first model and the second model by using the OES raw data; and predicting quality of a wafer produced by a semiconductor process by using the selected model, wherein the first model is trained by tokens generated by grouping the OES raw data according to wavelength ranges selected based on a material used in the semiconductor process and a time range selected to correspond to a specific process step of the semiconductor process, the specific process step to be analyzed during the semiconductor process.

12 . The method of claim 11 , wherein
the selecting of the model to be used in quality prediction includes inputting the OES raw data to the first model and the second model to generate a first quality index predicted value and a second quality index predicted value, respectively; generating a first coefficient of determination based on a quality index measured value and the first quality index predicted value; generating a second coefficient of determination based on a quality index measured value and the second quality index predicted value; comparing sizes of the first coefficient of determination and the second coefficient of determination; and selecting the first model as the model to be used in quality prediction when the first coefficient of determination is greater than the second coefficient of determination, and selecting the second model as the model to be used in quality prediction in other cases.

13 . The method of claim 11 , wherein
the grouping of the OES raw data includes dividing the time range into a time section with a predetermined size, and grouping the OES raw data so that one token corresponds to one wavelength and the time section.

14 . The method of claim 11 , wherein
the grouping of the OES raw data includes dividing the time range into a time section with a predetermined size, and grouping the OES raw data so that one token corresponds to wavelengths and the time section.

15 . The method of claim 11 , wherein
the second model is trained using OES data processed by applying self-normalization, calibration, or an intensity average ratio at a time interval.

16 . A device for predicting quality of a workpiece by executing instructions loaded on a memory device through one or more processors,
wherein the memory device provides a model based on optical emission spectroscopy (OES) raw data, and the instructions select a subset of the OES raw data that (i) is within wavelength ranges pre-associated with a material used during a process of producing the workpiece and that (ii) is within a timespan corresponding to a specific step of the process, generates tokens according to the subset of the OES raw data, and trains the model with the tokens.

17 . The device of claim 16 , wherein the wavelength ranges are defined according to wavelengths of OES responsiveness of the material.

18 . The device of claim 16 , wherein the wavelength ranges are selected based on having intensities greater than a predetermined reference.

19 . The device of claim 16 , wherein
the generating of the tokens includes dividing the time range into a time section with a predetermined size, and grouping the subset of OES raw data so that one token corresponds to one wavelength range and the time section.

20 . The device of claim 16 , wherein
the generating of tokens includes dividing the time range into a time section with a predetermined size, and grouping the subset of OES raw data so that one token corresponds to wavelength ranges and the time section.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Sim Deokwon
• Baek Sangwon
• You Kaeweon
• Lee Hyunsoo
• Choi Byungyong
• Kim Jonghyun
• Hong Jongik

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0455

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

• G01J3/027 Primary

  Control of working procedures of a spectrometer; Failure detection; Bandwidth calculation · CPC title

• G01J3/443

  Emission spectrometry · CPC title

• G01J2003/1213

  Filters in general, e.g. dichroic, band · CPC title