A Deep Learning-Based Screening Method for Improving the Quality and Reliability of Integrated Passive Devices

Chien Hui Chuang, Kuan Wei Hou, Cheng Wen Wu, Mincent Lee, Chia Heng Tsai, Hao Chen, Min Jer Wang

研究成果: Conference contribution

摘要

Integrated passive devices (IPDs) have been widely used in advanced packaging of semiconductor chips, to improve their power integrity and impedance matching. There is a growing demand in guaranteeing signal and power integrity for the chips used in safety-critical products, such as those used in automotive, aviation, industrial, and defense systems, where IPDs help improve quality and reliability of the chips. Therefore, IPD testing and screening itself is essential. Note that the cost of replacing failed IPDs is much higher than the cost of manufacturing them, so screening bad IPDs before mounting is also crucial. In this work, we propose a machine learning (ML) based screening methodology to identifying the IPDs that have potential reliability issues. Based on the parametric data of 360,000 IPDs collected from the wafer probing test, the proposed Semiconductor Quality Net (SQnet) is trained to predict the IPDs which have low breakdown voltage, i.e., low reliability. Keeping the overkill rate below 10%, our method can screen out 6 to 15X more bad dies than the existing industrial methods, i.e., DPAT and GDBC.

原文English
主出版物標題Proceedings - 2020 IEEE International Test Conference in Asia, ITC-Asia 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面13-18
頁數6
ISBN(電子)9781728189444
DOIs
出版狀態Published - 2020 九月
事件4th IEEE International Test Conference in Asia, ITC-Asia 2020 - Taipei, Taiwan
持續時間: 2020 九月 232020 九月 25

出版系列

名字Proceedings - 2020 IEEE International Test Conference in Asia, ITC-Asia 2020

Conference

Conference4th IEEE International Test Conference in Asia, ITC-Asia 2020
國家Taiwan
城市Taipei
期間20-09-2320-09-25

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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