TCAD-Enabled Machine Learning-An Efficient Framework to Build Highly Accurate and Reliable Models for Semiconductor Technology Development and Fabrication

The requirements on data-driven Machine Learning models for industrial applications are often stricter, compared to those used for academic purposes, as model reliability is critical in industrial environments. Herein is introduced a framework which enables automated data generation with the goal of...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2023-05, Vol.36 (2), p.268-278
Main Authors: Jungmann, Paul, Johnson, Jeffrey B., Silva, Eduardo C., Taylor, William, Khan, Abdul Hanan, Kumar, Akash
Format: Article
Language:English
Subjects:
Data models
Data points
Data processing
Fabrication
fabrication capabilities
Industrial applications
Integrated circuit modeling
Machine learning
Mathematical models
Model accuracy
Parameters
Reliability
Semiconductor devices
Semiconductor process modeling
TCAD
TCAD-eML
TCAD-enabled machine learning
technology computer-aided design
Training data
Transistors
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Summary:The requirements on data-driven Machine Learning models for industrial applications are often stricter, compared to those used for academic purposes, as model reliability is critical in industrial environments. Herein is introduced a framework which enables automated data generation with the goal of efficiently providing a data set sufficient to build a reliable and actionable model. Essential to this framework is the placement of the model training/testing data points, which need to be well distributed across the defined input parameter space. The framework is applied to semiconductor fabrication, wherein TCAD, a set of simulation tools that reproduce the physical processing and the final electrical performance of semiconductor devices, is a well-established capability. Transistor-level processing data is reproduced with TCAD simulations, from which the Machine Learning model is built. The framework described here assures that the resulting Machine Learning model fulfills the accuracy requirements across the parameter space. As an example application, the final Machine Learning model is then used to modify the process for a transistor, to obtain both better electrical performance and reduced variability.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2023.3240033