Statistical peak temperature prediction and thermal yield improvement for 3D chip multiprocessors

Da Cheng Juan, Siddharth Garg, Diana Marculescu

Research output: Contribution to journalArticlepeer-review


Thermal issues have become critical roadblocks for achieving highly reliable three-dimensional (3D) integrated circuits (ICs). The presence of process variations further exacerbates these problems. In this article, we propose techniques for the efficient evaluation and mitigation of the impact of leakage power variations on the temperature profile of 3D Chip Multiprocessors (CMPs). Experimental results demonstrate that, due to the impact of process variations, a 4-tier 3D implementation can be more than 40?C hotter and 23% leakier than its 2D counterpart. To determine the maximum temperature of each fabricated 3D IC, we propose an accurate learning-based model for peak temperature prediction. Based on the learning model, we then propose two post-fabrication techniques to increase the thermal yield of 3D CMPs: (1) tier restacking and (2) thermally-Aware die matching. Experimental results show that: (1) the proposed prediction model achieves more than 98% accuracy, and (2) the proposed thermally-Aware, post-fabrication optimization techniques significantly improve the thermal yield from only 51% to 99% for 3D CMPs.

Original languageEnglish (US)
Article number39
JournalACM Transactions on Design Automation of Electronic Systems
Issue number4
StatePublished - Aug 2014


  • 3D IC
  • Chip multiprocessor
  • Leakage power
  • Prediction
  • Process variation
  • Statistical model
  • Temperature
  • Thermal hotspot

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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