Interpolation and difference optimized machine learning model for accurate prediction of silicon etching depth with small sample dataset

Abstract

A novel interpolation and difference optimized (IDO) machine learning model to predict the depth of silicon etching is proposed, which is particularly well-suited to addressing small sample problems. Our approach involves dividing both experimental and simulation data obtained from the Technology Computer-Aided Design (TCAD) software into training and testing sets. Both experimental data and TCAD simulation data are used as inputs to machine learning module 1 (ML1), while ML2 takes the actual experimental data as inputs and then learns the difference between the experimental data and the TCAD simulation data, outputting the difference. The outputs generated by ML1 and ML2 serve as input parameters to machine learning module 3 (ML3), and the weights of ML3 are updated through its own learning process to produce the final prediction results. We demonstrate that our IDO model, which contains three basic ML algorithms, achieves higher prediction accuracy compared to the basic ML algorithm alone. Moreover, through ablation studies, we establish that the three components of the IDO prediction model are inseparable. The IDO model exhibits improved generalization performance, making it particularly suitable for small sample datasets in the semiconductor processing domain.