Layout Resynthesis by Applying Design-for-manufacturability Guidelines to Avoid Low-coverage Areas of a Cell-based Design

Author:

Wang Naixing1,Pomeranz Irith1,Reddy Sudhakar M.2,Sinha Arani3,Venkataraman Srikanth3

Affiliation:

1. Purdue University, West Lafayette, IN, USA

2. University of Iowa, Iowa City, IA, USA

3. Intel Corporation, Hillsboro, OR, USA

Abstract

Design-for-manufacturability (DFM) guidelines are recommended layout design practices intended to capture layout features that are difficult to manufacture correctly. Avoiding such features prevents the occurrence of potential systematic defects. Layout features that result in DFM guideline violations may not be avoided completely due to the design constraints of chip area, performance, and power consumption. A framework for translating DFM guideline violations into potential systematic defects, and faults, was described earlier. In a cell-based design, the translated faults may be internal or external to cells. In this article, we focus on undetectable faults that are external to cells. Using a resynthesis procedure that makes fine changes to the layout while maintaining the design constraints, we target areas of the design where large numbers of external faults related to DFM guideline violations are undetectable. By eliminating the corresponding DFM guideline violations, we ensure that the circuit does not suffer from low-coverage areas that may result in detectable systematic defects escaping detection, but failing the circuit in the field. The layout resynthesis procedure is applied to benchmark circuits and logic blocks of the OpenSPARC T1 microprocessor. Experimental results indicate that the improvement in the coverage of potential systematic defects is significant.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Subject

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

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CmpCNN: CMP Modeling with Transfer Learning CNN Architecture;ACM Transactions on Design Automation of Electronic Systems;2022-10-27

2. Automating Design For Yield: Silicon Learning to Predictive Models and Design Optimization;2020 IEEE International Test Conference (ITC);2020-11-01

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