Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices

Oh, Misook; Lee, Kitae; Kim, Sihyun; Park, Byung‐Gook

doi:10.1016/j.engappai.2022.105546

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Considering this, the research paper [9] has included an effectual data-driven methodology as NB (Naiive Bayes) for multi-objective optimization with the use of NN-based sensitivity evaluation for semiconductor devices. By undertaking sensitivity analysis, influential parameters can be quickly identified.…”

Section: Process Optimizationmentioning

confidence: 99%

A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production

Das

2024

IJCE

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Purpose: Exploring AI techniques to improve the quality control of semiconductor production brings numerous advantages, such as enhanced precision, heightened efficiency, and early detection of issues, cost reduction, continuous enhancement, and a competitive edge. These benefits establish this area of research and its practical application in the semiconductor industry as valuable and worthwhile. Methodology: It aims to highlight the advancements, methodologies employed, and outcomes obtained thus far. By scrutinizing the current state of research, the primary objective of this paper is to identify significant challenges and issues associated with AI approaches in this domain. These challenges encompass data quality and availability, selecting appropriate algorithms, interpreting AI models, and integrating them with existing production systems. It is vital for researchers and industry professionals to understand these challenges to effectively address them and devise effective solutions. Moreover, it aims to lay the groundwork for future researchers, offering them a theoretical framework to devise potential solutions for enhancing quality control in semiconductor production. This review aims to drive a research on the semi-conductor production with the AI techniques to enhance the Quality control. Findings: The main findings to offer research is more efficient and accurate approach compared to traditional manual methods, leading to improved product quality, reduced costs, and increased productivity. Armed with this knowledge, future researchers can design and implement innovative AI-driven solutions to enhance quality control in semiconductor production. Unique contribution to theory, policy and practice: Overall, the theoretical foundation presented in this paper will aid researchers in developing novel solutions to improve quality control in the semiconductor industry, ultimately leading to enhanced product reliability and customer satisfaction.

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Section: Process Optimizationmentioning

confidence: 99%