隨著資通訊技術、人工智慧以及大數據分析的進步並運用數據支援決策，智慧製造應用之可行性與重要性日益增加。因此，在全球智慧製造需求的驅動下，半導體產業是少數處於成長模式的關鍵產業之一。藉由製造生產設備之模擬、虛實整合系統(CPS，Cyber-Physical Systems)以及分散式決策，並將其整合入智慧工廠，可成為降低成本、提高生產力以及產品品質的基石,同時此形式之整合也可能對工業界帶來執行上的挑戰。舉例來說，量測感應儀器、機器人和CPS的資料流可以使製造更加智慧。相對的，對智慧平台之需求也將增加，智慧平台透過建模、優化和決策傳遞製造數據的價值，故此類需求可能會大幅增加。

本研究以數據驅動決策與優化，並為半導體工業之智慧製造設計智慧決策支援系統為目標，著眼於半導體工業智慧決策支援系統之設計與需求研究，關注數據支援決策和優化應用。此外，本研究以學習演算法為主軸探討兩個晶圓製造過程中的決策議題分別為良率提升之要因檢測以及高混合擾動系統的先進製程控制的設計。本研究藉由實證研究與討論實務的智能決策支援系統的應用與發展，並討論未來研究方向。

With advances in information and telecommunication technologies, Artificial Intelligent, big data, and data-driven decision making, smart manufacturing can be an essential component of competitive advantages and sustainable development. In the era of the smart world, the semiconductor industry is one of the few global industries that are in a growth mode to smartness due to worldwide demand. The promising significant opportunities to reduce cost, boost productivity, and improve quality in wafer manufacturing are based on the integration or combination of simulated replicas of actual equipment, Cyber-Physical Systems and regionalized or decentralized decision making into a smart factory. However, this integration also presents the industry with novel unique challenges. The stream of the data from sensors, robots, and Cyber-Physical Systems can aid to make the manufacturing smart. Therefore, there is an increasing need for designing a smart platform for modeling, optimization, and decision making to the value delivery from manufacturing data. This research aims to design an intelligent decision support system for the semiconductor industry with a focus on data enabled decision making and optimization applications. We investigate on design and essential needs for the foundation of an intelligent decision support system for smart manufacturing emphasizing on the semiconductor industry. Thereafter, a number of sophisticated challenges of decision making in the wafer fabrication process, root cause detection for yield enhancement, and design of advanced process control for the high-mixed disturbed system are discussed with a focus on learning based algorithm. We conclude this research by discussing research directions for future studies.

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