Application of machine learning for optimization of 3-D integrated circuits and systems

Sung Joo Park, Bumhee Bae, Joungho Kim, Madhavan Swaminathan

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


The 3-D integration helps improve performance and density of electronic systems. However, since electrical and thermal performance for 3-D integration is related to each other, their codesign is required. Machine learning, a promising approach in artificial intelligence, has recently shown promise for addressing engineering optimization problems. In this paper, we apply machine learning for the optimization of 3-D integrated systems where the electrical performance and thermal performance need to be analyzed together for maximizing performance. In such systems, modeling can be challenging due to the multiscale geometries involved, which increases computation time per iteration. In this paper, we show that machine learning can be applied to such systems where multiple parameters can be optimized to achieve the desired performance using the minimum number of iterations. These results have been compared with other promising optimization methods in this paper. The results show that on an average, 4.4%, 31.1%, and 6.9% improvement in temperature gradient, CPU time, and skew are possible using machine learning, as compared with other methods.

Original languageEnglish (US)
Article number7850943
Pages (from-to)1856-1865
Number of pages10
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number6
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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