Selective Use of Stitch-Induced Via for V0 Mask Reduction: Standard Cell Design and Placement Optimization von HYUN, Daijoon, JUNG, Younggwang, SHIN, Youngsoo

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Accurate Interpolation of Library Timing Parameters Through Recurrent Convolutional Neural Network von Hyun, Daijoon, Jung, Younggwang, Shin, Youngsoo

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Decap Insertion With Local Cell Relocation Minimizing IR-Drop Violations and Routing DRVs von Hyun, Daijoon, Jung, Younggwang, Shin, Youngsoo

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Airgap Insertion and Layer Reassignment Under Setup and Hold Timing Constraints von Hyun, Daijoon, Jung, Younggwang, Shin, Youngsoo

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Routability Optimization of Extreme Aspect Ratio Design through Non-uniform Placement Utilization and Selective Flip-flop Stacking von Hyun, Daijoon, Koh, Sunwha, Jung, Younggwang, Kim, Taeyoung, Shin, Youngsoo

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SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance von Kim, Hui Kwon, Kim, Younggwang, Lee, Sungtae, Min, Seonwoo, Bae, Jung Yoon, Choi, Jae Woo, Park, Jinman, Jung, Dongmin, Yoon, Sungroh, Kim, Hyongbum Henry

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Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity von Kim, Hui Kwon, Min, Seonwoo, Song, Myungjae, Jung, Soobin, Choi, Jae Woo, Kim, Younggwang, Lee, Sangeun, Yoon, Sungroh, Kim, Hyongbum (Henry)

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DeepCpf1: Deep learning-based prediction ofCRISPR-Cpf1 activity at endogenous sites von Hui Kwon, Kim, Seonwoo, Min, Myungjae, Song, Soobin, Jung, Jae Woo, Choi, Younggwang, Kim, Sangeun, Lee, Sungroh, Yoon, Hyongbum Henry, Kim

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DeepCpf1: Deep learning-based prediction of CRISPR-Cpf1 activity atendogenous sites

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DeepCpf1: Deep learning-based prediction ofCRISPR-Cpf1 activity at endogenous sites von Kwon, Kim Hui, Seonwoo, Min, Myungjae, Song, Soobin, Jung, Woo, Choi Jae, Younggwang, Kim, Sangeun, Lee, Sungroh, Yoon, Henry, Kim Hyongbum

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