ASSESSMENT OF SUCCESS INDICATORS ASSOCIATED WITH MANUFACTURING IC CHIPS IN INDIAN SEMICONDUCTOR INDUSTRY
Received: 1st February 2024 Revised: 23rd February 2024 and 28th February 2024 Accepted: 1st February 2024
Keywords:
Integrated Circuit (IC), T-Test, Success Indicators, , Manufacturing, Cost, Time, QualityAbstract
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(2), 664–678. https://doi.org/10.1016/J.TELE.2016.10.008
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(2), 38–51. https://www.researchgate.net/publication/334895496
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. International Journal of Production Research, 58(9), 2784–2804. https://doi.org/10.1080/00207543.2020.1733125
Chung, E., Park, K., & Kang, P. (2023). Fault classification and timing prediction based on shipment inspection data and maintenance reports for semiconductor manufacturing equipment. Computers & Industrial Engineering, 176, 108972. https://doi.org/10.1016/J.CIE.2022.108972
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. https://doi.org/10.1016/J.SEGAN.2020.100408
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. International Journal of Production Research, 60(2), 742–765. https://doi.org/10.1080/00207543.2021.1976433
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(4), 1925–1936. https://doi.org/10.1109/TASE.2020.2983061
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. Computers & Industrial Engineering, 183, 109449. https://doi.org/10.1016/J.CIE.2023.109449
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. https://doi.org/10.1109/ICIT46573.2021.9453481
Guin, U., Forte, D., & Tehranipoor, M. (2016). Design of accurate low-cost on-chip structures for protecting integrated circuits against recycling. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 24(4), 1233–1246. https://doi.org/10.1109/TVLSI.2015.2466551
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(3), 241–253. https://doi.org/10.1080/00401706.1997.10485116
Hickey, P., & Kozlovski, E. (2020). E-strategies for aftermarket facilitation in the global semiconductor manufacturing industry. Journal of Enterprise Information Management, 33(3), 457–481. https://doi.org/10.1108/JEIM-05-2019-0124/FULL/XML
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. https://doi.org/10.1109/ACCESS.2020.3019332
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(3), 439–458. https://doi.org/10.1007/S40171-023-00347-Y/TABLES/7
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. https://www.researchgate.net/publication/337732542
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(07). https://www.researchgate.net/publication/344082931
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. https://doi.org/10.1109/ACCESS.2020.3034680
Khakifirooz, M., Fathi, M., & Wu, K. (2019). Development of Smart Semiconductor Manufacturing: Operations Research and Data Science Perspectives. IEEE Access, 7, 108419–108430. https://doi.org/10.1109/ACCESS.2019.2933167
Kim, T. K. (2015). T test as a parametric statistic. Korean Journal of Anesthesiology, 68(6), 540–546. https://doi.org/10.4097/KJAE.2015.68.6.540
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. Journal of Manufacturing Systems, 52, 146–156. https://doi.org/10.1016/J.JMSY.2019.07.001
MacK, C. A. (2011). Fifty years of Moore’s law. IEEE Transactions on Semiconductor Manufacturing, 24(2), 202–207. https://doi.org/10.1109/TSM.2010.2096437
Mousavi, B. A., Azzouz, R., & Heavey, C. (2019). MATHEMATICAL MODELLING OF PRODUCTS ALLOCATION TO CUSTOMERS FOR SEMICONDUCTOR SUPPLY CHAIN. Procedia Manufacturing, 38, 1042–1049. https://doi.org/10.1016/J.PROMFG.2020.01.190
Nagapurkar, P., & Das, S. (2022). Economic and embodied energy analysis of integrated circuit manufacturing processes. Sustainable Computing: Informatics and Systems, 35, 100771. https://doi.org/10.1016/J.SUSCOM.2022.100771
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(2), 250–256. https://doi.org/10.1109/TSM.2019.2897690
Pai, F. Y., & Yeh, T. M. (2013). Effective implementation for introducing ISO/TS 16949 in semiconductor manufacturing industries. Total Quality Management & Business Excellence, 24(3–4), 462–478. https://doi.org/10.1080/14783363.2012.728854
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(11), 1751–1766. https://doi.org/10.1108/JBIM-08-2019-0354/FULL/XML
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(2), 224. https://doi.org/10.3390/APP8020224
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. https://doi.org/10.1109/ICIEA49774.2020.9101925
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.” Environmental Research, 206, 112263. https://doi.org/10.1016/J.ENVRES.2021.112263
Raithatha, M., & Bapat, V. (2014). Accounting Standards Compliance: Comparison between Manufacturing and Service Sector Companies from India. International Journal of Economics and Finance, 6(9). https://doi.org/10.5539/ijef.v6n9p158
