A Survey on Machine Learning in Lithography

Mansi Phute, Aditi Sahastrabudhe, Sameer Pimparkhede, Shubham Potphode, Kshitij Rengade, Swati Shilaskar
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引用次数: 2

Abstract

Lithography is the process of transferring the geometric patterns from the masks to the resist material on the semiconductor. It is a very important part of VLSI fabrication that is critical when it comes to the efficient functioning of circuits. Many state-of-the-art methods use Machine Learning (ML) to identify lithography patterns that can cause issues in the future as these algorithms can predict defects in patterns which the machine has not encountered before. This paper focuses on the need for Machine Learning in the lithography process, and the various algorithms used like Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). There are multiple applications including Hotspot detection, Optical Proximity Correction (OPC), Sub Resolution Assist Feature (SRAF), Phase Shift Masks (PSM), and Resist Modelling. The major issue faced by Machine Learning algorithms is that of false positives. It can be reduced by utilizing the Gaussian process after initial detection.
光刻中的机器学习研究综述
光刻是将几何图案从掩模转移到半导体上的抗蚀剂材料的过程。它是VLSI制造中非常重要的一部分,对于电路的有效运行至关重要。许多最先进的方法使用机器学习(ML)来识别将来可能导致问题的光刻模式,因为这些算法可以预测机器以前没有遇到过的模式缺陷。本文重点介绍了在光刻过程中对机器学习的需求,以及使用的各种算法,如支持向量机(SVM)、人工神经网络(ANN)和卷积神经网络(CNN)。有多种应用,包括热点检测,光学接近校正(OPC),亚分辨率辅助功能(SRAF),相移掩模(PSM)和抗阻建模。机器学习算法面临的主要问题是误报。在初始检测后,可以利用高斯过程来减小它。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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