Convolutional Neural Network-Assisted Photoresist Formulation Discriminator Design of a Contact Layer for Electron Beam Lithography

The photoresist formulation is closely related to the material properties, and its composition content determines the lithography imaging quality. To satisfy the process requirements, imaging verification of extensive formulations is required through lithography experiments. Identifying photoresist formulations with a high imaging performance has become a challenge. Herein, we develop a formulation discriminator of a metal oxide nanoparticle photoresist for a contact layer applied to electron beam lithography. This discriminator consists of convolutional neural network photoresist imaging and formulation classification models. A photoresist imaging model is adopted to predict the contact width of variable formulations, while a formulation classification model is used to classify formulations according to relative local critical dimension uniformity (RLCDU). The verification results indicate that the discriminator can accurately recognize photoresist formulations that simultaneously meet the conditions of contact width and RLCDU, and its feasibility has been demonstrated, providing a valuable reference for the preparation of photoresist materials.