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Surrogate Model Development for Digital Experiments in Welding

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Soft-Sensing Regression Model: From Sensor to Wafer Metrology Forecasting.

Angzhi Fan¹, Yu Huang², Fei Xu³

¹Department of Statistics, University of Chicago, Chicago, IL 60637, USA.

Sensors (Basel, Switzerland)

|October 28, 2023

Summary

This study introduces a machine learning model for predicting semiconductor inspection measurements using sensor data. The Long Short-term Memory network effectively forecasts quality metrics, improving semiconductor manufacturing processes.

Keywords:

artificial intelligence data processing wafer manufacturing

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Area of Science:

Semiconductor Manufacturing
Machine Learning
Metrology Systems

Background:

The semiconductor industry demands advanced inspection and metrology for yield, quality, and cost reduction.
Real-time monitoring via sensors in manufacturing equipment enables machine learning applications.
Soft sensing for predictive metrology is crucial for process optimization.

Purpose of the Study:

To address the soft-sensing regression problem in metrology systems.
To predict upcoming inspection measurements using in-process sensor data.
To develop an accurate and early prediction model for semiconductor manufacturing.

Main Methods:

Proposed a Long Short-term Memory (LSTM) network-based regressor.
Developed two distinct loss functions for model training.
Introduced a novel piece-wise evaluation metric for accuracy assessment.

Main Results:

The LSTM model achieved accurate and early predictions for various inspection types.
Experimental results demonstrated the model's effectiveness in complex manufacturing processes.
The proposed evaluation metric provided a mathematical approach to assess prediction accuracy.

Conclusions:

The developed soft-sensing regression model enhances predictive capabilities in semiconductor metrology.
The LSTM network offers a powerful tool for real-time quality prediction in manufacturing.
This approach facilitates proactive adjustments, improving overall semiconductor production efficiency.