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An efficient partial sampling inspection for lot sentencing based on process yield

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Abstract

As the process yield has significantly raised because of the advanced development of manufacturing technology today, engineers would logically attempt to inspect fewer sample items for the quality evaluation of processes or products. Therefore, in this paper, an efficient sampling inspection method based on the process yield index Spk is developed for lot sentencing, wherein the inspection is performed only on a fractional submitted lot rather than examining every following submission. Both the average sample number (ASN) and operating characteristic (OC) functions of the proposed method are derived on the basis of the Markov chain technique. Further, an optimization model that minimizes the ASN and constrains two OC functions restricted to given quality requirements and tolerable risks is constructed. Performance comparisons in terms of economy and discriminatory power are analyzed by contrasting ASN and OC curves with existing Spk-based methods under the same quality conditions to emphasize the superiority of the proposed method. For easy implementation, we prove the applicability of the proposed method by demonstrating a case study taken from an integrated circuit packaging company.

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Acknowledgements

The earlier version of this paper was presented at 27th International Society of Science and Applied Technologies (ISSAT) conference on Reliability & Quality in Design.

Funding

This work is partially supported by National Science and Technology Council, Taiwan under Grant No. NSTC 111-2221-E-167-012.

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Authors and Affiliations

  1. College of Management, National Chin-Yi University of Technology, Taichung, Taiwan

    Shih-Wen Liu

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Chien-Wei Wu

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  1. Shih-Wen Liu
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Correspondence to Chien-Wei Wu.

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Liu, SW., Wu, CW. An efficient partial sampling inspection for lot sentencing based on process yield. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05341-2

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