HotspotFusion: A Generative AI Approach to Predicting CMP Hotspot in Semiconductor Manufacturing

IF 2.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-12-02 DOI:10.1109/TSM.2024.3510376

Hsiu-Hui Hsiao;Kung-Jeng Wang

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引用次数: 0

Abstract

The semiconductor industry thrives on rapid technological advancements, crucial for superior product performance and cost efficiency. Chip design houses and consumer electronics companies must continuously pursue New Tape Out (NTO) to maintain technological leadership. Timely NTO completion expedites product launches, crucial in the competitive semiconductor market. This paper addresses Chemical Mechanical Polishing (CMP) hotspot, critical in NTO quality and cycle time, affecting wafer surface topology. Hotspot defects can degrade wafer performance, demanding swift detection and resolution. Traditional methods can only identify CMP hotspot after manufacturing, necessitating repeated adjustments to IC design. We propose HotspotFusion, leveraging pattern density data from Graphic Design System (GDS) to predict CMP hotspot early in the design phase. Utilizing a generative AI model, HotspotFusion significantly reduces NTO cycle time by enabling proactive hotspot detection and process optimization, fostering efficiency and competitiveness in semiconductor manufacturing.

查看原文本刊更多论文

热点融合：一种预测半导体制造CMP热点的生成式人工智能方法

半导体行业在快速的技术进步中蓬勃发展，这对卓越的产品性能和成本效率至关重要。芯片设计公司和消费电子公司必须不断追求新磁带（NTO），以保持技术领先地位。及时完成NTO可以加快产品发布，这在竞争激烈的半导体市场中至关重要。化学机械抛光（CMP）是影响晶圆表面拓扑结构和NTO质量和周期的关键问题。热点缺陷会降低晶圆的性能，需要快速检测和解决。传统方法只能在制造后才能识别CMP热点，需要对IC设计进行反复调整。我们提出HotspotFusion，利用图形设计系统（GDS）的模式密度数据在设计阶段早期预测CMP热点。HotspotFusion利用生成式人工智能模型，通过实现主动热点检测和流程优化，显著缩短了NTO周期时间，提高了半导体制造的效率和竞争力。

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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来源期刊

IEEE Transactions on Semiconductor Manufacturing 工程技术-工程：电子与电气

CiteScore

5.20

自引率

11.10%

发文量

101

审稿时长

3.3 months

期刊介绍： The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.