重定目标
计算机科学
平版印刷术
计算光刻
光学接近校正
反向
人工智能
计算机工程
多重图案
抵抗
过程(计算)
纳米技术
视觉艺术
图层(电子)
材料科学
几何学
艺术
操作系统
数学
作者
Marco Guajardo,Ahmed S. Omran,Howard Clark
摘要
The goal of this paper is to explore machine learning solutions to improve the run-time of model-based retargeting in the mask synthesis flow. The purpose of retargeting is to re-size non-lithography friendly designs so that the design geometries are shifted to a more lithography-robust design space. However, current model-based approaches can take significant run-time. As a result, this step is rarely done in production settings. Different machine learning solutions for resolution enhancement techniques (RETs) have been previously proposed. For instance, to model optical proximity correction (OPC) or inverse lithography (ILT). In this paper, we compare and expand some of these solutions. In the end, we will discuss the experimental results that can achieve a nearly 360x run-time improvement while maintaining similar accuracy to traditional retargeting techniques.
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