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Creators/Authors contains: "Lamichhane, S"

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  1. ; ; (, IEEE Computer Society Annual Symposium on VLSI)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proc. of the 2020 ACM/IEEE Workshop on Machine Learning for CAD (MLCAD'21))
    Accepted Manuscript Full Text Available
  3. ; ; ; (, Proc. of the 2020 ACM/IEEE Workshop on Machine Learning for CAD (MLCAD'21))
    In this paper, we propose an image generative learning framework for electrostatic analysis for VLSI dielectric aging estimation. This work leverages the observation that the synthesized multi layer interconnect VLSI layout can be viewed as layered 2D images and the analysis can be viewed as the image generation. The efficient image-to-image translation property of generative learning is therefore used to obtain the potential distribution on the respective interconnect layers. Compared with the recent CNN-based electrostatic analysis method, the new method can lead to 1.54x speedup for inference due to reduced neural network structures and parameters. We demonstrate the proposed method for time-dependent dielectric breakdown analysis and show the significant speedup compared to the traditional numerical method. 
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    Accepted Manuscript Full Text Available