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Meta-Learned and TCAD-Assisted Sampling in Semiconductor Laser Annealing
Tejender Singh Rawat1, Chung Yuan Chang1, Yen-Wei Feng1
1Institute of Electronics Engineering, National Yang Ming Chiao Tung University, 300, Hsinchu City30010, Taiwan, R.O.C.
ACS Omega
|January 16, 2023
Summary
This study introduces a TCAD-assisted meta-learned sampling method for efficient semiconductor manufacturing. It significantly reduces experimental trials and improves predictive model accuracy, outperforming pure ML approaches.
Area of Science:
- Semiconductor Manufacturing
- Machine Learning
- Computational Modeling
Background:
- Machine learning (ML) application in semiconductor manufacturing is common, but efficient search space sampling remains underexplored for critical processes like lithography, annealing, deposition, and etching.
- Advanced semiconductor processes are costly, making efficient experimental design crucial for developing accurate predictive models with minimal trials.
Purpose of the Study:
- To propose and validate a novel technology computer-aided design (TCAD)-assisted meta-learned sampling approach for optimizing semiconductor manufacturing processes.
- To minimize experimental trials required for constructing accurate predictive models in complex manufacturing environments.
Main Methods:
- Development of a meta-learner that dynamically adjusts the hybridization strategy between TCAD simulations and ML for selecting optimal sampling points.
- Implementation and testing of the TCAD-assisted meta-learned sampling algorithm using laser annealing as a case study.
Main Results:
- The proposed TCAD-assisted meta-learned sampling method achieved significantly lower mean square error (MSE) within the initial 100 sampling steps compared to a pure ML approach.
- The TCAD-assisted sampling approach prevented MSE degradation observed in pure TCAD methods between 200-400 sampling steps, demonstrating enhanced model stability.
Conclusions:
- The TCAD-assisted meta-learned sampling approach offers a more efficient and accurate method for predictive modeling in semiconductor manufacturing.
- This methodology holds potential for broader applications in other manufacturing sectors and applied machine intelligence fields.
