Session information

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Oral

[Session 8] Machine Learning

Thu. Sep 28, 2023 1:00 PM - 2:40 PM Hall B

Chair: Satofumi Souma (Kobe Univ.), William Vandenberghe (UT Dallas)

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1:00 PM - 1:20 PM

[Session_8-01] FlowSim: An Invertible Generative Network for Efficient Statistical Analysis under Process Variations

Chanwoo Park¹, *Hong Chul Nam^1,2, Jihun Park¹, Jongwook Jeon^1,3 (1. Alsemy Inc., 2. ETH Zurich, 3. Sungkyunkwan Univ.)

1:20 PM - 1:40 PM

[Session_8-02] Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction

Thomas Lu¹, Albert Lu¹, *Hiu Yung Wong¹ (1. San Jose State University)

1:40 PM - 2:00 PM

[Session_8-03] A Novel Machine-Learning Based Mode Space Method for Efficient Device Simulations

*Yeongjun Lim¹, Mincheol Shin¹ (1. KAIST)

2:00 PM - 2:20 PM

[Session_8-04] Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Green’s Function Simulations

*Preslav Aleksandrov Aleksandrov¹, Ali Rezaei¹, Nikolas Xeni¹, Tapas Dutta¹, Asen Asenov¹, Vihar Georgiev¹ (1. Univ. of Glasgow)

2:20 PM - 2:40 PM

[Session_8-05] Statistical Modeling of Metal-Oxide RRAM SET/RESET Behavior Using Deep Neural Networks

*Aarav Wattal¹, Akash Levy¹, Zainab Faryal Khan¹ (1. Stanford Univ.)

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