Proceedings PaperMask synthesis using machine learning software and hardware platforms
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Inspired by many success stories of machine learning (ML) in a broad range of artificial intelligence (AI) applications, both industrial and academic researchers are now actively developing ML solutions for challenging problems in computational lithography. In this work, we explore the possibility of utilizing ML software and hardware platforms for mask synthesis applications. Specifically, we demonstrate a standalone mask synthesis flow that runs entirely on the TensorFlow ML platform with a reinforcement learning (RL) approach and GPU acceleration. We will describe the architecture of our ML mask synthesis framework that comprises separable and interchangeable components including neural network (NN)-based 3D mask, imaging and resist models. We will discuss the readiness of these components and present the proof-of-concept evaluation results of the proposed ML mask synthesis framework.