Meta-Learned and TCAD-Assisted Sampling in Semiconductor Laser Annealing

Abstract

While applying machine learning (ML) to semiconductor manufacturing is prevalent, an efficient way to sample the search space has not been explored much in key processes such as lithography, annealing, deposition, and etching. The aim is to use the fewest experimental trials to construct an accurate predictive model. Here, we proposed a technology computer added design (TCAD)-assisted meta-learned sampling approach. The meta-learner adjusts the way of sampling in terms of how to hybridize the TCAD with ML when selecting the next sampling point. While an advanced semiconductor process is expensive, efficient sampling is indispensable. Using laser annealing as an example, we demonstrate the effectiveness of the proposed algorithm where the mean square error (MSE) at the first 100 sampling steps using TCAD-assisted meta-learned sampling is significantly lower than the pure ML approach. Besides, with reference to the pure TCAD approach, the TCAD-assisted sampling prevents the MSE degradation at 200−400 sampling steps. The proposed approach can be used in other manufacturing or even any applied machine intelligence fields.