Hierarchical Decoupling Capacitor Optimization for Power Distribution Network of 2.5D ICs with Co-Analysis of Frequency and Time Domains Based on Deep Reinforcement Learning
Yuanyuan Duan, Haiyang Feng, Zhiping Yu, Hanming Wu, Leilai Shao, Xiaolei Zhu

TL;DR
This paper presents a novel deep reinforcement learning-based two-phase optimization method for decoupling capacitor placement in 2.5D IC power networks, addressing both frequency and time domain noise issues for improved robustness.
Contribution
It introduces the first dual-domain optimization strategy combining frequency and time domain analysis for decap placement in 2.5D ICs using deep reinforcement learning.
Findings
Effective reduction of small signal noise and SSN.
Improved PDN robustness in 2.5D ICs.
First dual-domain optimization approach for decap placement.
Abstract
With the growing need for higher memory bandwidth and computation density, 2.5D design, which involves integrating multiple chiplets onto an interposer, emerges as a promising solution. However, this integration introduces significant challenges due to increasing data rates and a large number of I/Os, necessitating advanced optimization of the power distribution networks (PDNs) both on-chip and on-interposer to mitigate the small signal noise and simultaneous switching noise (SSN). Traditional PDN optimization strategies in 2.5D systems primarily focus on reducing impedance by integrating decoupling capacitors (decaps) to lessen small signal noises. Unfortunately, relying solely on frequency-domain analysis has been proven inadequate for addressing coupled SSN, as indicated by our experimental results. In this work, we introduce a novel two-phase optimization flow using deep…
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Taxonomy
Topics3D IC and TSV technologies · Electromagnetic Compatibility and Noise Suppression · Radio Frequency Integrated Circuit Design
MethodsFocus
