Tackling data inconsistency and runtime issues in inverse lithography technology (ILT) with comparative convergence study

This study delves into the convergence challenges of Inverse Lithography Technology (ILT) in advancing Optical Proximity Correction (OPC). While ILT shows promise, it faces runtime hurdles and intricate stitching issues caused by data inconsistencies at tile boundaries, particularly in complex corrections. Here, we perform a comprehensive and comparative analysis of the run time and data consistency at tile boundaries for four gradient descent-based algorithms: Steepest Descent (SD), Momentum, Adaptive gradient, and Adaptive Moment Estimation (Adam). Our findings reveal that stitching problems arise from insufficient ambient range and convergence issues during ILT optimization. We recommend using an ambient size equal to or larger than the kernel size. Furthermore, we show that robust convergence can mitigate data inconsistency challenges, even with a limited ambient range. Notably, Adam emerges as a powerful solution, offering substantial runtime acceleration, often ten to hundreds of times faster than SD. Renowned for its prowess in optimizing complex models and GPU-accelerated parallel processing, Adam is a key strategy for expediting computational lithography in semiconductor manufacturing, paving the way for future advancements in ILT.