Policy Gradient Reinforcement Learning-based Optimal Decoupling Capacitor Design Method for 2.5-D/3-D ICs using Transformer Network-Reference-Cited by-同舟云学术

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Policy Gradient Reinforcement Learning-based Optimal Decoupling Capacitor Design Method for 2.5-D/3-D ICs using Transformer Network

Published:2020-12-14 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS)
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Author:

Park Hyunwook,Kim Minsu,Kim Subin,Jeong Seungtaek,Kim Seongguk,Kang Hyungmin,Kim Keunwoo,Son Keeyoung,Park Gapyeol,Son Kyungjune,Shin Taein,Kim Joungho

Funder

National Research Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9312833/9312882/09312908.pdf?arnumber=9312908

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SI/PI Co-Design of 12.8 Gbps HBM I/O Interface using Bayesian Optimization for PSIJ Reduction;2023 IEEE Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMC+SIPI);2023-07-29

2. Power Distribution Network Impedance Analysis considering Thermal Distribution;2022 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS);2022-12-12

3. Transformer Network-Based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM);IEEE Transactions on Microwave Theory and Techniques;2022-11

4. Scalable Transformer Network-based Reinforcement Learning Method for PSIJ Optimization in HBM;2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS);2022-10-09

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