ASSESSMENT OF SUCCESS INDICATORS ASSOCIATED WITH MANUFACTURING IC CHIPS IN INDIAN SEMICONDUCTOR MANUFACTURING INDUSTRY

Authors

  • Karam Bharat Singh Department of Management Sciences, Indian Institute of Technology Kanpur, Kalyanpur-208016, Uttar Pradesh, India; Department of Technology Management for Innovation, The University of Tokyo, Hongo, Bunkyo City, Tokyo 113-8654, Japan
  • Subhas Chandra Misra Department of Management Sciences, Indian Institute of Technology Kanpur, Kalyanpur-208016, Uttar Pradesh, India

DOI:

https://doi.org/10.20319/icssh.2024.116127

Keywords:

Semiconductors, Integrated Circuit (IC), T-Test, Success Indicators

Abstract

Integrated circuit plays a crucial role in reducing the size, increasing the processing speed, and enhancing the dependability on the electronic devices. Notably, the widespread use of these technologies has led to advancements in various sectors, including the communications, healthcare, and automobile industry. This study rank and identifies the critical success indicators associated with the manufacturing IC chips in the Indian semiconductor industry by employing one sample t-test approach. Based on the existing literature, the study investigates sixteen success indicators associated with the manufacturing IC chips in India. In addition, experts from the semiconductor manufacturing organization have validated these factors concerning the Indian semiconductor industry. The research concludes that “Monitor the Time-to-Market (SI7)”, “Enhance Customer Satisfaction (SI13)”, “Assess the Yield Rate (SI11)”, and “Calculate the Return on Investment (ROI) for Cost (SI5)”, are the critical success indicators associated with manufacturing of IC chips, as per the t-test analysis from 152 respondents working in the semiconductor sectors. The findings have multiple implications for businesses and policymaker, and can assist various stakeholders, including global semiconductor companies, domestic manufacturers, and fabless semiconductor firms

References

Alharthi, A., Alassafi, M.O., Walters, R.J., Wills, G.B., 2017. An exploratory study for investigating the critical success factors for cloud migration in the Saudi Arabian higher education context. Telematics and Informatics 34, 664–678.

Chandra Misra, S., Balmukund Rahi, S., Bisui, S., Singh, A., 2019. Factors Influencing the Success of Cloud Adoption in the Semiconductor Industry. Software Quality Professional 21, 38–51.

Chattopadhyay, S., Pal, S., 2017. Availability of Infrastructure Facilities in India: Prospects and Challenges. Availability of Infrastructure Facilities in India: Prospects and Challenges 143–163.

Chien, C.F., Lin, Y.S., Lin, S.K., 2020. Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor. Int J Prod Res 58, 2784–2804.

Chung, E., Park, K., Kang, P., 2023. Fault classification and timing prediction based on shipment inspection data and maintenance reports for semiconductor manufacturing equipment. Comput Ind Eng 176, 108972.

Das, S., Mao, E., 2020. The global energy footprint of information and communication technology electronics in connected Internet-of-Things devices. Sustainable Energy, Grids and Networks 24, 100408.

Dreyfus, P.A., Psarommatis, F., May, G., Kiritsis, D., 2022. Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework. Int J Prod Res 60, 742–765.

Fan, S.K.S., Hsu, C.Y., Tsai, D.M., He, F., Cheng, C.C., 2020. Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing. IEEE Transactions on Automation Science and Engineering 17, 1925–1936.

Feng, N., Zhang, Y., Ren, B., Dou, R., Li, M., 2023. How Industrial Internet Platforms guide high-quality information sharing for semiconductor manufacturing? An evolutionary game model. Comput Ind Eng 183, 109449.

Fischer, D., Moder, P., Ehm, H., 2021. Investigation of Predictive Maintenance for Semiconductor Manufacturing and its Impacts on the Supply Chain. Proceedings of the IEEE International Conference on Industrial Technology 2021-March, 1409–1416.

Guin, U., Forte, D., Tehranipoor, M., 2016. Design of accurate low-cost on-chip structures for protecting integrated circuits against recycling. IEEE Trans Very Large Scale Integr VLSI Syst 24, 1233–1246.

Hansen, M.H., Nair, V.N., Friedman, D.J., 1997. Monitoring wafer map data from integrated circuit fabrication processes for spatially clustered defects. Technometrics 39, 241–253.

Hickey, P., Kozlovski, E., 2020. E-strategies for aftermarket facilitation in the global semiconductor manufacturing industry. Journal of Enterprise Information Management 33, 457–481.

Hsu, P.Y., Yeh, I.W., Tseng, C.H., Lee, S.J., 2020. A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance. IEEE Access 8, 156208–156218.

Ishak, S., Shaharudin, M.R., Salim, N.A.M., Zainoddin, A.I., Deng, Z., 2023. The Effect of Supply Chain Adaptive Strategies During the COVID-19 Pandemic on Firm Performance in Malaysia’s Semiconductor Industries. Global Journal of Flexible Systems Management 24, 439–458.

Jain, V., Chawla, C., Arya, S., Agarwal, R., Agarwal, M., 2019. An Empirical Study of Product Design for New Product Development with Special Reference to Indian Mobile Industry. TEST Engineering & Management 81, 1241–1254.

Jamil, J.M., Nizal, I., Shaharanee, M., Faizal, A., Fazil, M., Sheng, A.J., 2020. Framework to Reduce Cost Scrapping and Cost of Assemble Test Capacity in Semiconductor Integrated Circuit Manufacturing. Framework 62.

Jiang, D., Lin, W., Raghavan, N., 2020. A novel framework for semiconductor manufacturing final test yield classification using machine learning techniques. IEEE Access 8, 197885–197895.

Khakifirooz, M., Fathi, M., Wu, K., 2019. Development of Smart Semiconductor Manufacturing: Operations Research and Data Science Perspectives. IEEE Access 7, 108419–108430.

Kim, T.K., 2015. T test as a parametric statistic. Korean J Anesthesiol 68, 540–546.

Lee, D.H., Yang, J.K., Lee, C.H., Kim, K.J., 2019. A data-driven approach to selection of critical process steps in the semiconductor manufacturing process considering missing and imbalanced data. J Manuf Syst 52, 146–156.

MacK, C.A., 2011. Fifty years of Moore’s law. IEEE Transactions on Semiconductor Manufacturing 24, 202–207.

Mousavi, B.A., Azzouz, R., Heavey, C., 2019. MATHEMATICAL MODELLING OF PRODUCTS ALLOCATION TO CUSTOMERS FOR SEMICONDUCTOR SUPPLY CHAIN. Procedia Manuf 38, 1042–1049.

Nagapurkar, P., Das, S., 2022. Economic and embodied energy analysis of integrated circuit manufacturing processes. Sustainable Computing: Informatics and Systems 35, 100771.

Nakazawa, T., Kulkarni, D. V., 2019. Anomaly detection and segmentation for wafer defect patterns using deep Convolutional Encoder-Decoder Neural Network Architectures in Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing 32, 250–256.

Pai, F.Y., Yeh, T.M., 2013. Effective implementation for introducing ISO/TS 16949 in semiconductor manufacturing industries. Total Quality Management & Business Excellence 24, 462–478.

Park, C., 2020. Market entry strategies in a high-tech successive generations market: a case study of three semiconductor firms with different entry modes. Journal of Business and Industrial Marketing 35, 1751–1766.

Park, S.H., Kim, S., Baek, J.G., 2018. Kernel-Density-Based Particle Defect Management for Semiconductor Manufacturing Facilities. Applied Sciences 2018, Vol. 8, Page 224 8, 224.

Prasetyo, Y.T., Veroya, F.C., 2020. An Application of Overall Equipment Effectiveness (OEE) for Minimizing the Bottleneck Process in Semiconductor Industry. 2020 IEEE 7th International Conference on Industrial Engineering and Applications, ICIEA 2020 345–349.

Pulicherla, K.K., Adapa, V., Ghosh, M., Ingle, P., 2022. Current efforts on sustainable green growth in the manufacturing sector to complement “make in India” for making “self-reliant India.” Environ Res 206, 112263.

Raithatha, M., Bapat, V., 2014. Accounting Standards Compliance: Comparison between Manufacturing and Service Sector Companies from India. Int J Econ Finance 6.

Ramani, V., Ghosh, D., Sodhi, M.S., 2022. Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry R. Omega (Westport) 113, 102720.

Roy, S., Modak, N., Dan, P.K., 2020. Role of Product Development Process for NPD Success in Indian Manufacturing Industries: Quality, Cost and Technological Aspects. Lecture Notes on Multidisciplinary Industrial Engineering Part F166, 583–596.

Ruberti, M., 2023. The chip manufacturing industry: Environmental impacts and eco-efficiency analysis. Science of The Total Environment 858, 159873.

Saif M Khan, Alexander Mann, Dahlia Peterson, 2021. The Semiconductor Supply Chain: Assessing National Competitiveness. Center for Security and Emerging Technology 8.8.

Sanders, R., 1987. The pareto principle: Its use and abuse. Journal of Services Marketing 1, 37–40.

Sansone, C., Hilletofth, P., Eriksson, D., 2020. Critical Operations Capabilities for Competitive Manufacturing in a High-Cost Environment: A Multiple Case Study. Operations and Supply Chain Management: An International Journal 13, 94–107.

Saqlain, M., Abbas, Q., Lee, J.Y., 2020. A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes. IEEE Transactions on Semiconductor Manufacturing 33, 436–444.

Saqlain, M., Jargalsaikhan, B., Lee, J.Y., 2019a. A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 32, 171–182.

Saqlain, M., Jargalsaikhan, B., Lee, J.Y., 2019b. A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 32, 171–182.

Saraswat, V.K., 2022. India Semiconductor Mission Boosting India’s Semiconductor and Display Ecosystem. NIScPR-CSIR, India 59, 19–21.

Schneider, G., Patolla, P., Fehr, M., Reichelt, D., Zoghlami, F., Delsing, J., 2022. Micro Service based Sensor Integration Efficiency and Feasibility in the Semiconductor Industry. Infocommunications Journal 14, 79–85.

Senoner, J., Netland, T., Feuerriegel, S., 2021a. Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing. Manage Sci 68, 5704–5723.

Senoner, J., Netland, T., Feuerriegel, S., 2021b. Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing. Manage Sci 68, 5704–5723.

Singh, M.K., Kumar, H., Gupta, M.P., Madaan, J., 2018. Competitiveness of Electronics manufacturing industry in India: an ISM–fuzzy MICMAC and AHP approach. Measuring Business Excellence 22, 88–116.

Tassey, G.C., Gallaher, M.P., Rowe, B.R., Rogozhin, A. V, Houghton, S.A., Davis, J.L., Lamvik, M.K., Geikler, J.S., 2007. Economic Impact of Measurement in the Semiconductor Industry Final Report Prepared for.

Umeda, S., Tamaki, K., Sumiya, M., Kamaji, Y., 2021. Planned Maintenance Schedule Update Method for Predictive Maintenance of Semiconductor Plasma Etcher. IEEE Transactions on Semiconductor Manufacturing 34, 296–300.

Zhu, K., Wen, C., Aljarb, A.A., Xue, F., Xu, X., Tung, V., Zhang, X., Alshareef, H.N., Lanza, M., 2021. The development of integrated circuits based on two-dimensional materials. Nature Electronics 2021 4:11 4, 775–785.

Downloads

Published

2024-01-31

How to Cite

Singh, K. B., & Misra, S. C. (2024). ASSESSMENT OF SUCCESS INDICATORS ASSOCIATED WITH MANUFACTURING IC CHIPS IN INDIAN SEMICONDUCTOR MANUFACTURING INDUSTRY. PEOPLE: International Journal of Social Sciences, 116–127. https://doi.org/10.20319/icssh.2024.116127