Advanced Risk Assessment and Mitigation Techniques for Semiconductor Manufacturing and Supply Chain Resilience

Paper ID: EIJTEM_2021_8_4_26-32

Author's Name: Dileep Kumar Ghattamaneni

Volume: 8

Issue: 4

Year: 2021

Page No: 26-32

Abstract:

Semiconductor fabrication includes intricate processes susceptible to errors; conventional Fault detection and diagnosis (FDD) techniques find it difficult to handle nonlinear, dynamic, and uncertain process dynamics. To address this issue, this study proposes a Probabilistic Empowering Spiking Decision Neural Networks with Pine Cone Optimization Algorithm (PRSDNNets+PCOA) approach to FDD in semiconductor production. The input data are collected from the SECOM dataset and these data are preprocessed by the Adaptive and Propagated Mesh Filtering (APMF) technique. After preprocessing, the feature extraction process is performed by the A New Discrete Cosine-Krawtchouk-Tchebichef Transform (ANDCKTT). Fault detection and diagnosis using Probabilistic Empowering Spiking Decision Neural Networks (PRSDNNets) and optimization through Pine Cone Optimization Algorithm (PCOA). The efficiency of the proposed PRSDNNets+PCOA model is tested with an accuracy rate of 99.9% and specificity of 99.8%. The proposed model is realized using the Python programming language. The result of the proposed approach provides credible fault detection, improved diagnostic accuracy, and efficient treatment of nonlinear and uncertain manufacturing process behaviors.

Keywords: Adaptive and Propagated Mesh Filtering, A New Discrete Cosine-Krawtchouk-Tchebichef Transform, Fault detection and diagnosis, Probabilistic Empowering Spiking Decision Neural Networks, Pine Cone Optimization Algorithm.

References:

1. Chen, Y.J., Wang, B.C., Wu, J.Z., Wu, Y.C. and Chien, C.F., 2017, August. Big data analytic for multivariate fault
detection and classification in semiconductor manufacturing. In 2017 13th IEEE Conference on Automation Science and
Engineering (CASE) (pp. 731-736). IEEE..
2. Fan, S.K.S., Hsu, C.Y., Tsai, D.M., He, F. and Cheng, C.C., 2020. Data-driven approach for fault detection and
diagnostic in semiconductor manufacturing. IEEE Transactions on Automation Science and Engineering, 17(4), pp.1925-
1936.
3. Munirathinam, S. and Ramadoss, B., 2016. Predictive models for equipment fault detection in the semiconductor
manufacturing process. IACSIT International Journal of Engineering and Technology, 8(4), pp.273-285.
4. Fan, S.K.S., Lin, S.C. and Tsai, P.F., 2016. Wafer fault detection and key step identification for semiconductor
manufacturing using principal component analysis, AdaBoost and decision tree. Journal of Industrial and Production
Engineering, 33(3), pp.151-168.
5. Chien, C.F., Liu, C.W. and Chuang, S.C., 2017. Analysing semiconductor manufacturing big data for root cause detection
of excursion for yield enhancement. International Journal of Production Research, 55(17), pp.5095-5107.
6. Zhang, Chuxu, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng
Chen, and Nitesh V. Chawla. "A deep neural network for unsupervised anomaly detection and diagnosis in multivariate
time series data." In Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, pp. 1409-1416. 2019.
7. He, R., Chen, G., Sun, S., Dong, C. and Jiang, S., 2020. Attention-based long short-term memory method for alarm rootcause diagnosis in chemical processes. Industrial & Engineering Chemistry Research, 59(25), pp.11559-11569.
8. Shen, Y., Abubakar, M., Liu, H. and Hussain, F., 2019. Power quality disturbance monitoring and classification based on
improved PCA and convolution neural network for wind-grid distribution systems. Energies, 12(7), p.1280.
9. Liu, B., Cao, J., Wang, W., Ma, N., Li, B., Liu, L. and Liu, X., 2018. Propagated mesh normal filtering. Computers &
Graphics, 74, pp.119-125.
10. Rabie, T. and Kamel, I., 2017. High-capacity steganography: a global-adaptive-region discrete cosine transform
approach. Multimedia Tools and Applications, 76, pp.6473-6493.
11. Tavanaei, A., Ghodrati, M., Kheradpisheh, S.R., Masquelier, T. and Maida, A., 2019. Deep learning in spiking neural
networks. Neural networks, 111, pp.47-63.
12. Dong, M., Zheng, H., Zhang, Y., Shi, K., Yao, S., Kou, X., Ding, G. and Guo, L., 2019. A novel maintenance decision
making model of power transformers based on reliability and economy assessment. IEEE access, 7, pp.28778-28790.
13. Zafar, M., Van Vinh, N., Behera, S.K. and Park, H.S., 2017. Ethanol mediated As (III) adsorption onto Zn-loaded pinecone
biochar: experimental investigation, modeling, and optimization u

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