Optical Diffraction-based Convolution for Semiconductor Mask Optimization

Young-Han Son, Dong-Hee Shin, Deok-Joong Lee, Hyun Jung Lee, Tae-Eui Kam

Keywords: AI4Science, Semiconductor, Mask Optimization, Optical Diffraction

TL;DR: An optical physics-driven neural network for semiconductor mask optimization

Abstract: In recent years, the increasing demand for smaller and more powerful semiconductors highlighted the critical role of lithography—a key stage in semiconductor manufacturing responsible for precise mask design and wafer patterning. To meet these demands, the semiconductor industry has increasingly adopted computational lithography, employing machine learning and deep learning techniques to accelerate advancements in lithographic technology. Despite the various research efforts and successes in computational lithography, there remains a lack of explicit incorporation of physical principles. This gap limits the ability of existing methods to fully capture the complex physical phenomena inherent in lithography behaviors. To bridge this gap, we propose OptiCo, a novel convolutional neural network that seamlessly integrates optical diffraction principles into its architecture. At its core, OptiCo employs an optical phase kernel to model phase variations resulting from light propagation, effectively capturing the physical interactions among light, masks, and wafers. We evaluate OptiCo on semiconductor lithography benchmarks, demonstrating its superior performance in mask optimization tasks, with its remarkable generalization capabilities in OOD datasets.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 10445