基于生成对抗网络的小光刻胶缺陷样本增强

Guang Yang, Zhihang Li, Zhijia Yang, Shuping Cui
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引用次数: 1

摘要

光刻胶涂层技术是半导体晶圆表面处理的重要组成部分。光刻胶中气泡的存在会严重影响晶圆的质量,然而,由于缺乏足够的气泡样品,使得智能自动检测技术成为不可能。为了解决这一问题,我们提出了基于对抗生成网络的B-GAN,并设计了用于潜在编码映射的映射网络和用于高质量气泡图像生成的合成网络,从而自动生成缺陷气泡样本。实验证明,该方法取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network
Photoresist coating technology is an important part of the surface treatment of semiconductor wafers. The presence of bubbles in photoresist can seriously affect the quality of wafers, however, the lack of sufficient bubble samples makes intelligent automatic detection techniques impossible. To solve such a problem, we propose B-GAN based on adversarial generative network, and design a mapping network for potential encoding mapping and a synthetic network for high-quality bubble image generation, so that defective bubble samples can be automatically generated. Experiments prove that our method achieves excellent results.
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