Machine learning in electron beam lithography to boost photoresist formulation design for high-resolution patterning

Abstract

A high-precision photoresist imaging model and formulation optimizer for electron beam lithography are developed. The optimized photoresist formulation meets the preset imaging performance requirement, boosting photoresist material design.

Machine Learning Applied to Electron Beam Lithography to Accelerate Process Optimization of a Contact Hole Layer

“…Machine learning (ML) is one of the most intelligent and cutting-edge modeling methods in artificial intelligence (AI), which studies how to use computers to simulate or implement human learning activities . Recently, researchers have begun to pay attention to an ML method, that is, long short-term memory (LSTM) networks, and use them to build predictive models for fuel cells, financial market, stock market, soft sensor, wind power, photovoltaic power, and solar irradiance. − It is worth noting that many scholars have demonstrated that the LSTM network − with long-term memory function has better prediction ability than multilayer perceptron neural network (MPNN), ,,− which is one of the most commonly used ML modeling methods. Therefore, in this article, we use an LSTM network to establish a lithographic imaging prediction model.…”