A Digital Twin Framework With Meta- and Transfer Learning for Scalable Multi-Machine Modeling and Optimization in Semiconductor Manufacturing

A Digital Twin Framework With Meta- and Transfer Learning for Scalable Multi-Machine Modeling and Optimization in Semiconductor Manufacturing

Despite recent advances in Digital Twin (DT) technologies for semiconductor manufacturing, no existing research convincingly demonstrates a unified, rapidly scalable, and data-efficient DT framework that can effectively handle stringent multi-objective optimization under severe data scarcity. This a...

Full description

Saved in:
Bibliographic Details
Main Authors: Chin-Yi Lin, Tzu-Liang Tseng, Tsung-Han Tsai
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Digital twin
meta-learning
transfer learning
Gaussian process model
deep feature extractor
multi-machine modeling
Online Access:https://ieeexplore.ieee.org/document/10993442/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Despite recent advances in Digital Twin (DT) technologies for semiconductor manufacturing, no existing research convincingly demonstrates a unified, rapidly scalable, and data-efficient DT framework that can effectively handle stringent multi-objective optimization under severe data scarcity. This absence of a comprehensive approach impedes widespread industry adoption, given the pressing need for flexible, universal solutions that rapidly adapt to diverse machines and processes. This study introduces MOODFG-MLTL, an innovative algorithm that integrates Meta-Learning and Transfer Learning within a Multi-Objective Optimization using Deep-Feature Gaussian Processes (MOODFG) architecture. By harnessing a centralized meta-model repository and dynamically refining surrogate models, the proposed framework efficiently leverages limited data and transfers knowledge across heterogeneous manufacturing configurations. In our experimental validation with Epi SiC processes, MOODFG-MLTL achieves near-optimal wafer thickness and doping control using only 15 samples—reducing data requirements by approximately 50% while converging up to 40% faster compared to conventional approaches. Experimental validation in epitaxial silicon carbide (Epi SiC) scenarios demonstrates that the MOODFG-MLTL not only maintains robust performance under constrained data conditions but also accelerates the deployment of data-efficient, multi-objective DT solutions. This advancement provides a critical foundation for achieving universal, intelligent DT implementations in the semiconductor industry’s evolving landscape.
ISSN:2169-3536

Similar Items