Abstract
Photolithography is a process used in the manufacturing of dies, which are at the core of complex integrated circuits. During this process several layers of semi-conducting material are stacked on top of each other. Precise alignment of the layers is crucial to the performance of a die. Upon completion, each die is subjected to several electrical tests. If many dies of a wafer fail the test, the whole wafer is considered faulty and has to be discarded, or reworked). This paper proposes the use of machine learning models to predict the outcome of a crucial test for MOS power leakage, from misalignments of a single layer. Wafers which are predicted to be faulty when finished can be predictively discarded, saving costs and resources otherwise spent on finishing the faulty wafer.
Original language | English |
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Pages (from-to) | 1508-1515 |
Number of pages | 8 |
Journal | Procedia Computer Science |
Volume | 200 |
DOIs | |
Publication status | Published - 2022 |
Event | 3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 - Linz, Austria Duration: 19 Nov 2021 → 21 Nov 2021 |
Bibliographical note
Publisher Copyright:© 2022 The Authors. Published by Elsevier B.V.
Funding
The work presented in this paper has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826589. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Netherlands, Belgium, Germany, France, Italy, Austria, Hungary, Romania, Sweden and Israel.
Keywords
- Automated manufacturing
- Industry 4.0
- machine learning
- power leakage
- prescriptive analytics