PoLM: Point Cloud and Large Pre-trained Model Catch Mixed-type Wafer Defect Pattern Recognition

	PoLM: Point Cloud and Large Pre-trained Model Catch Mixed-type Wafer Defect Pattern Recognition
	Hongquan He1 ; Guowen Kuang 2; Qi Sun 3; Geng H(耿浩)1
	2024
会议录名称	2024 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION, DATE
ISSN	1530-1591
发表状态	已发表
摘要	As the technology node scales down to 5nm/3nm, the consequent difficulty has been widely lamented. The defects on the surface of wafers are much more prone to emerge during manufacturing than ever. What's worse, various single-type defect patterns may be coupled on a wafer and thus shape a mixed-type pattern. To improve yield during the design cycle, mixed-type wafer defect pattern recognition is required to perform to identify the failure mechanisms. Based on these issues, we revisit failure dies on wafer maps by treating them as point sets in two-dimensional space and propose a two-stage classification framework, PoLM. The challenge of noise reduction is considerably improved by first using an adaptive alpha-shapes algorithm to extract intricate geometric features of mixed-type patterns. Unlike sophisticated frameworks based on CNNs or Transformers, PoLM only completes classification within a point cloud cluster for aggregating and dispatching features. Furthermore, recognizing the remarkable success of large pre-trained foundation models (e.g., OpenAI's GPT-n series) in various visual tasks, this paper also introduces a training paradigm leveraging these pre-trained models and fine-tuning to improve the final recognition. Experiments demonstrate that our proposed framework significantly surpasses the state-of-the-art methodologies in classifying mixed-type wafer defect patterns.
会议名称	27th Design, Automation and Test in Europe Conference and Exhibition (DATE)
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	null,Valencia,SPAIN
会议日期	MAR 25-27, 2024
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	Shanghai Pujiang Program[22PJ1410400]
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:001253778900205
出版者	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/357344
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_耿浩
通讯作者	Geng H(耿浩)
作者单位	1.ShanghaiTech University 2.Shenzhen Polytechnic 3.Zhejiang University
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Hongquan He,Guowen Kuang,Qi Sun,et al. PoLM: Point Cloud and Large Pre-trained Model Catch Mixed-type Wafer Defect Pattern Recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2024.