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[1] 缇庡湅APC澶ф渻锛歨ttps://www.apcconference.com锛涙瓙娲睞PC|M澶ф渻锛歨ttps://www.apcm-europe.eu/home/锛涗簽娲� AEC/APC 鐮旇◣鏈冿細https://www.semiconportal.com/AECAPC
[2] J. Moyne銆丣. Samantaray鍜孧.Armacost锛屻�婃噳鐢ㄤ簬鍗婂皫楂斿埗閫犲厛閫插伐钘濇帶鍒剁殑澶ф暩(sh霉)鎿�(j霉)鑳藉姏銆嬶紙Big Data Capabilities Applied to Semiconductor Manufacturing Advanced Process Control锛�锛孖EEE Transactions on Semiconductor Manufacturing锛岀 29 鍗凤紝绗� 4 鏈�锛�2016 骞� 11 鏈堬紝绗� 283-291 闋�
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[4] J.Moyne鍜孞.Iskandar锛屻�婃櫤鑳藉埗閫犵殑澶ф暩(sh霉)鎿�(j霉)鍒嗘瀽鎶�琛擄細鍗婂皫楂斿埗閫犳渚嬬爺绌躲�嬶紙Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing锛�锛孭rocesses Journal锛岀 5 鍗�锛岀 3 鏈�锛�2017 骞� 7 鏈�銆傚彲鍦ㄧ窔鏌ラ柋锛歨ttp://www.mdpi.com/2227- 9717/5/3/39/htm
[5]銆婂湅闅涘櫒浠跺拰绯荤当(t菕ng)鎶�琛撹棈鍦栵細宸ュ粻闆嗘垚鐧界毊鏇搞�嬶紙International Roadmap for Devices and Systems (IRDS): Factory Integration White Paper锛�锛�2018 骞寸増?锛熺◢璇扳偓鏌ラ柋锛歨ttps://irds.ieee.org/
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[8] Lammers, D.銆婂ぇ鏁�(sh霉)鎿�(j霉)鑸囩缍撶恫绲★細鍗婂皫楂旂敘妤�(y猫)鏂伴﹨鍕曞姏銆嬶紙Big Data and Nueral Networks: New Drivers for the Semiconductor Industry锛�锛屻�奛ANOCHIP 鏅跺湏鍒堕�犲粻瑙ｆ焙鏂规銆�锛�2017 骞寸 12 鍗风 1 鏈燂紝绗� 22-28 闋�
[9] Vogel-Walcutt, J.J銆丟ebrim, J.B.銆丆. Bowers銆丆arper, T.M.銆丯icholson, D.锛屻�婅獚鐭ヨ矤鑽风悊璜栬垏寤烘涓荤京鏂规硶锛氫綍鑰呯湡姝ｉ�氬悜楂樻晥娣卞害瀛哥繏锛熴�嬶紙Cognitive Load Theory vs. Constructivist Approaches: Which Best Leads to Efficient, Deep Learning?锛夛紝 Journal of Computer Assisted Learning锛�2010?锛熺◢璇扳偓鏌ラ柋锛歨ttps://onlinelibrary.wiley.com/doi/10.1111/j.1365-2729.2010.00381.x
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