@article{SisLab4430, month = {March}, title = {HotCluster: A thermal-aware defect recovery method for Through-Silicon-Vias Towards Reliable 3-D ICs systems}, author = {Nam Khanh Dang and Akram Ben Ahmed and Abderazek Ben Abdallah and Xuan Tu Tran}, publisher = {IEEE}, year = {2021}, journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4430/}, abstract = {Through Silicon Via (TSV) is considered as the near-future solution to realize low-power and high-performance 3D-Integrated Circuits (3D-ICs) and 3D-Network-on-Chips (3DNoCs). However, the lifetime reliability issue of TSV due to its fault sensitivity and the high operating temperature of 3D-ICs, which also accelerates the fault-rate, is one of the most critical challenges. Meanwhile, most current works focus on detecting and correcting TSV defects after manufacturing without considering high-temperature nodes? impact on lifetime reliability. Besides, the recovery for defective clusters is also challenging because of costly redundancies. In this work, we present HotCluster: a hotspot-aware self-correction platform for clustering defects in 3D-NoCs to help understand and tackle this problem. We first give a method to predict normalized fault rates and place redundant TSV groups according to each region?s fault rate. In our particular medium fault-rate (normalized to the coolest area), HotCluster reduces about 60\% of the redundancies in comparison to the uniformly distributed redundancies while having a higher ratio of router working in a normal state. Furthermore, HotCluster integrates both online (weight-based) and offline (max-flow min-cut offline method) mapping algorithms to help the system correct the faulty TSV clusters. The experimental results show that both the max-flow min-cut offline method and weight-based online mode with a redundancy of 0.25 exhibits less than 1\% of routers disabled under 50\% defect-rates.} }