References
- Shukla M, Shankar R. An extended technology-organization-environment framework to investigate smart manufacturing system implementation in small and medium enterprises. Comput Ind Eng. 2022;163:107865. doi: 10.1016/j.cie.2021.107865
- Zhang X, Ming X, Bao Y. A flexible smart manufacturing system in mass personalization manufacturing model based on multi-module-platform, multi-virtual-unit, and multi-production-line. Comput Ind Eng. 2022;171:108379. doi: 10.1016/j.cie.2022.108379
- Fan SKS, Tsai DM, He F, et al. Key parameter identification and Defective wafer detection of semiconductor manufacturing processes using image processing techniques. IEEE Trans Semicond Manufact. 2019;32(4):544–552. doi: 10.1109/TSM.2019.2929765
- Fan SKS, Hsu CY, Jen CH, et al. Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes. Advan Eng Inform. 2020;46: 101166:1–12. doi: 10.1016/j.aei.2020.101166
- Fan SKS, Hsu CY, Tsai DM, et al. Data-driven approach for Fault detection and Diagnostic in semiconductor manufacturing. IEEE Trans Automat Sci Eng. 2020;17(4):1925–1936. doi: 10.1109/TASE.2020.2983061
- Tsai DM, Fan SKS, Chou YH. Auto-annotated deep segmentation for surface defect detection. IEEE Trans Instrum Meas. 2021;70(5011410):1–10. doi: 10.1109/TIM.2021.3087826
- Fan SKS, Jen CH, Lin WK. Data-driven optimization of accessory combinations for final testing processes in semiconductor manufacturing. J Manuf Syst. 2022;63:275–287. doi: 10.1016/j.jmsy.2022.03.014
- Fan SKS, Jen CH, Tsai DM, et al. Data visualization of Anomaly detection in semiconductor processing tools. IEEE Trans Semicond Manufact. 2022;35(2):186–197. doi: 10.1109/TSM.2021.3137982
- Fan SKS, Hsu CY, Tsai DM, et al. Key feature identification for monitoring wafer-to-wafer variation in semiconductor manufacturing. IEEE Trans Automat Sci Eng. 2022;19(3):1530–1541. doi: 10.1109/TASE.2022.3141426
- Jen CH, Fan SKS, Lin YY. Data-driven virtual metrology and re-training systems for color filter processes of TFT-LCD manufacturing. IEEE Trans Instrum Meas. 2022;71(1006912):1–12. doi: 10.1109/TIM.2022.3207807
- Fan SKS, Cheng CW, Tsai DM. Fault diagnosis of wafer acceptance test and chip probing between front-end-of-line and back-end-of-line processes. IEEE Trans Automat Sci Eng. 2022;19(4):3068–3082. doi:10.1109/TASE.2021.3106011
- Fan SKS, Tsai DM, Yeh PC. Effective variational-autoencoder-based generative models for highly imbalanced fault detection data in semiconductor manufacturing. IEEE Trans Semicond Manufact. 2023;36(2):205–214. doi: 10.1109/TSM.2023.3238555
- Fan SKS, Tsai DM, Shih YF. Self-assured semi-supervised learning for wafer defect pattern classification. IEEE Trans Semicond Manufact. 2023;36(3):404–415. doi: 10.1109/TSM.2023.3276816
- Sharma P, Liu H. A machine-learning-based data-centric misbehavior detection model for internet of vehicles. IEEE Internet Things J. 2021;8(6):4991–4999. doi: 10.1109/JIOT.2020.3035035
- Hamid OH. From model-centric to data-centric AI: a paradigm shift or rather a complementary approach? 8th Int Conf On Information Technology Trends (ITT). 2022;196–199.
- ITRI.org.tw, Intelligentization enabling technology, 2022. [cited 12 Feb 2023]. Available from: https://www.itri.org.tw/english/ListStyle.aspx?DisplayStyle=01&SiteID=1&MmmID=1071732317037114157.
- Nguyen G, Dlugolinsky S, Bobák M, et al. Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev. 2019;52(1):77–124. doi: 10.1007/s10462-018-09679-z
- Fan SKS, Chang XW, Lin YY. Product-to-product virtual metrology of color filter processes in panel industry. IEEE Trans Automat Sci Eng. 2022;19(4):3496–3507. doi: 10.1109/TASE.2021.3124157
- Ku CC, Chien CF, Ma KT. Digital transformation to empower smart production for industry 3.5 and an empirical study for textile dyeing. Comput Ind Eng. 2020;142:106297. doi: 10.1016/j.cie.2020.106297
- Zhu X, Ge S, Wang N. Digital transformation: a systematic literature review. Comput Ind Eng. 2021;162:107774. doi: 10.1016/j.cie.2021.107774
- Dall’ora N, Alamin K, Fraccaroli E, et al. Digital transformation of a production line: network design, Online data Collection and energy monitoring. IEEE Trans Emerg Topics Comput. 2022;10(1):46–59. doi: 10.1109/TETC.2021.3132432
- Baslyman M. Digital transformation from the industry perspective: definitions, goals, conceptual model, and processes. IEEE Access. 2022;10:42961–42970. doi: 10.1109/ACCESS.2022.3166937
- Haleem A, Javaid M, Singh RP, et al. Hyperautomation for the enhancement of automation in industries. Sens Int. 2021;2:100124. doi: 10.1016/j.sintl.2021.100124
- Landing.ai, AI transformation playbook how to lead your company into the AI era. 2022. [cited 11 Feb 2023]. Available from: https://landing.ai/resources/ai-transformation-playbook.
- Landing.ai, Understanding data-centric AI. 2022. [cited 23 Feb 2023]. Available from: https://landing.ai/data-centric-ai/.
- Rice R, Assisted intelligence vs. Augmented intelligence and autonomous intelligence. [ Online]. 2020 Available: https://fedtechmagazine.com/article/2020/01/assisted-intelligence-vs-augmented-intelligence-and-autonomous-intelligence-perfcon. [Accessed. 10- February- 2023].
- Li D, Fast-Berglund A, Paulin D, et al. Exploration of digitalized presentation of information for Operator 4.0: Five industrial cases. Comput Ind Eng. 2022;168:108048. doi: 10.1016/j.cie.2022.108048
- Gartner.com, Information technology: Gartner Glossary. 2022. [Cited 22 Feb 2023]. Available from: https://www.gartner.com/en/information-technology/glossary/digitalization.
- Brem A, Giones F, Werle M. The AI digital Revolution in innovation: a conceptual framework of artificial intelligence technologies for the management of innovation. IEEE Trans Eng Manage. 2023;70(2):770–776. doi: 10.1109/TEM.2021.3109983
- Martínez LR, Rios RAO, Prieto MD, Eds. New trends in the use of artificial intelligence for the industry 4.0.London, United Kingdom: IntechOpen; 2020. doi: 10.5772/intechopen.86015
- PwC.com, It’s time to get excited about boring AI. 2021. [cited 13 Feb 2023]. Available from: https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/publications/ai-automation-data-extraction.html?utm_campaign=sbpwc&utm_medium=site&utm_source=articlerelated.
- Entegris.com, Mask and Reticle Handling. 2022. [cited 2 Feb 2023]. Available from: https://www.entegris.com/en/home/products/mask-and-reticle-handling.html.
- Fan SKS, Jen CH, Hsu CY, et al. A new Double Exponentially Weighted Moving Average run-to-run control using a disturbance-accumulating strategy for mixed-Product mode. IEEE Trans Automat Sci Eng. 2021;18(4):1846–1860. doi: 10.1109/TASE.2020.3021949
- Kla.com, Product Families. 2022. [cited 4 Mar 2023]. Available from: https://www.kla.com/products.
- Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Machine Intell. 1995;17(8):790–799. doi: 10.1109/34.400568
- Radosavovic I, Kosaraju RP, Girshick R, et al. Designing network design spaces. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 10428–10436.
- Ester M, Kriegel H, Sander J, et al. A density-based algorithm for discovering clusters in Large Spatial databases with noise. Proc. of 2nd International Conference on Knowledge Discovery and Ming, Portland, Oregon; 1996. p. 226–231.
- Testi M, Ballabio M, Frontoni E, et al. MLOps: a taxonomy and a methodology. IEEE Access. 2022;10:63606–63618. doi: 10.1109/ACCESS.2022.3181730
- Chien CF, Hung WT, Pan CW, et al. Decision-based virtual metrology for advanced process control to empower smart production and an empirical study for semiconductor manufacturing. Comput Ind Eng. 2022;169:108245. doi: 10.1016/j.cie.2022.108245
- Jiang X, Ge Z. Data augmentation Classifier for imbalanced fault classification. IEEE Trans Autom Sci Eng. 2021;18(3):1206–1217. doi: 10.1109/TASE.2020.2998467
- Yuan X, Ou C, Wang Y, et al. A layer-wise data augmentation strategy for deep learning networks and its soft sensor application in an Industrial hydrocracking process. IEEE Trans Neural Net Learn Syst. 2021;32(8):3296–3305. doi: 10.1109/TNNLS.2019.2951708