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26 March 2019 OPC model accuracy study using high volume contour based gauges and deep learning on memory device
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Abstract
As the design node of memory device shrinks, OPC model accuracy is becoming ever more critical from development to manufacturing. To improve the model accuracy, more and more physical effects are analyzed and terms for those physical effects are added. But it is unachievable to capture the complete physical effects. In this study, deep neural network is employed and studied to improve model accuracy. Regularization is achieved using physical guidance model. To address overfitting issue, high volume of contour based edge placement (EP) gauges (>10K) are generated using fast eBeam tool (eP5) and metrology processing software (MXP) without increasing turnaround time. It is shown that the new approach improved model accuracy by >47% compared to traditional approach on >1.4K verification gauges.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Young-Seok Kim, SeIl Lee, Zhenyu Hou, Yiqiong Zhao, Meng Liu, Yunan Zheng, Qian Zhao, Daekwon Kang, Lei Wang, Mark Simmons, Mu Feng, Jun Lang, Byoung-Il Choi, Gilbert Kim, Hakyong Sim, Jongcheon Park, Gyun Yoo, JeonKyu Lee, Sung-woo Ko, Jaeseung Choi, Cheolkyun Kim, and Chanha Park "OPC model accuracy study using high volume contour based gauges and deep learning on memory device", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095913 (26 March 2019); https://doi.org/10.1117/12.2515274
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