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A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

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Abstract

The technology of big data analysis and artificial intelligence deep learning has been actively cross-combined with various fields to increase the effect of its original low single field. Precision components commonly used in electronic products use changes in the conductivity of semiconductors to process information. This study aims to review key milestones and recent developments in the semiconductor industry using artificial intelligence methods. For this systematic review, we searched academic networks between 2015 and 2022, including Nature, Elsevier, Springer, Taylor & Francis Online, Multidisciplinary Digital Publishing Institute, and the Institute of Electrical and Electronics Engineers. The literature reviewed is based on conference proceedings and journal articles, specifically covering the key achievements of the discussion paper, the key technologies used, experimental results, opportunities, and future research pathways. After searching on an academic website, we selected six major studies. In five of these studies, visual object detection, surface defect detection, machine production scheduling application, fault diagnosis and prediction, and monitoring of the manufacturing process were made using artificial neural networks, machine learning methods, and hybrid models. In addition, the studies covered independent, single methods or used more than two types of technologies for performance comparison. Finally, we reviewed the strengths and weaknesses of the literature. We also analysed various datasets, acquisition systems, and experimental scenarios. The review shows that as the number of studies conducted in manufacturing continues to increase, more research is needed to unearth key information that is often overlooked, all of which are challenges in refining science and overcoming real-world scenarios.

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Acknowledgements

The authors extend their gratitude for the support they received from the National Key R &D Program of China (2022YFB4701400/4701402), SZSTC Grant(JCYJ20190809172201639, WDZC20200820200655001), Shenzhen Key Laboratory (ZDSYS20210623092001004), Beijing Key Lab of Networked Multimedia, and the Information Engineering College, Yango University through the High-Level Talents Project under Grant CHN-F-IECY201871015.

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Author notes

  1. Sheng Hui Meng and Tian Jiun Huang have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, Tsinghua University, Haidian District, Beijing, 100190, China

    An Chi Huang

  2. Artificial Intelligence College, Yango University, Mawei District, Fuzhou, 350015, China

    Sheng Hui Meng

  3. Department of Computer Science and Information Engineering, National Kaohsiung University, Nanzih District, Kaohsiung, 81154, Taiwan

    Tian Jiun Huang

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  1. An Chi Huang
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  2. Sheng Hui Meng
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  3. Tian Jiun Huang
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Contributions

The authors contributed to the article in order of their appearance. AC and TJ were involved in idea generation, design, and implementation of experiments, and drafting of the article, whereas SH was involved in idea generation, results, discussion, and feedback, and final revision and drafting of the article.

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Correspondence to Sheng Hui Meng.

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Appendix A

Appendix A

See Table 8.

Table 8 The abbreviations and terms
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Huang, A.C., Meng, S.H. & Huang, T.J. A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges. Cluster Comput 26, 3437–3472 (2023). https://doi.org/10.1007/s10586-023-04115-6

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