光电器件缺陷检测辅助决策:一个真实的工业4.0案例研究

G. Moustris, G. Kouzas, Spyros Fourakis, Georgios Fiotakis, Apostolos Chondronasios, Abd Al Rahman M. Abu Ebayyeh, Alireza Mousavi, Kostas Apostolou, J. Milenkovic, Zoi Chatzichristodoulou, E. Beckert, J. Butet, S. Blaser, O. Landry, A. Müller
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引用次数: 0

摘要

本文提出了一种基于工业4.0概念的创新方法,通过采用图像处理和深度学习技术来检测缺陷,从而监测光电器件的生命周期。该系统包括光电器件生产过程前端部分的缺陷检测和分类,提供两阶段方法;第一个是在晶圆级别上对单个组件的实际缺陷识别,而第二个是基于识别到的缺陷对这些组件进行预分类。该系统提供了两个基于图像的缺陷检测管道。一种是使用低分辨率的光栅图像,另一种是使用显微镜获得的高分辨率表面扫描图像。为了使整个过程自动化,需要使用称为高级通信中间件(HLCM)的通信中间件来编排处理步骤之间的信息。在该过程的最后一步,决策支持系统(DSS)收集所有信息,处理它并标记额外的缺陷类型类别,以便向光电工程师提供建议。提出的解决方案已在激光制造的实际工业用例中实现。分析表明,通过所提出的过程验证的芯片具有比完全拒绝的芯片高6倍的特定频率的激光概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study
This paper presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects. The system provides two image-based defect detection pipelines. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. At the last step of the process, a Decision Support System (DSS) collects all information, processes it and labels it with additional defect type categories, in order to provide recommendations to the optoelectronical engineer. The proposed solution has been implemented on a real industrial use-case in laser manufacturing. Analysis shows that chips validated through the proposed process have a probability to lase at a specific frequency six times higher than the fully rejected ones.
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