Presentation
Self-Supervised Learning based Etching Process Modeling: Bridging Simulation and Experimental Data
DescriptionIn the integrated circuit industry, the precise etching process control is critical for realizing the ever scaled new devices. The development of etching process face new challenges and demanding for efficient etching simulation to gain insights into the etching mechanisms. This paper introduces a cross convolutional neural network (CCNN) tailored for predicting the etching profiles, which incorporates profile-oriented lateral convolution and temporal longitudinal convolution. By integrating autoencoder as an auxiliary task, we conduct self-supervised pre-training of lateral convolution layers using simulated data, resulting in comprehensive feature extraction and representation of profile. Fine-tuning is carried out on the temporal longitudinal convolution layers and subsequent fully connected layers with experimental data, to achieve precise prediction of experimental data. Experimental results validate the effectiveness of the novel neural network architecture and the self-supervised learning framework, yielding a reduction in average prediction errors on experimental data from 7.9028 nm to 6.3822 nm.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby