Precompensation, BIST and Analogue Berger Codes for Self-Healing of Neuromorphic RRAM

Tsung Chu Huang, Jeffae Schroff

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Neuromorphic RRAM has become the most promising candidate for AI applications. But it suffers three issues including degradation, defects and errors. To overcome the three issues we proposed a precompensation technique for compensating resistive degradation. A linear-system-based BIST architecture is developed with proposed diagonal sliding march test can effectively and efficiently screen out the uncompensated degradation and permanent defects. Analog Berger codes is proposed for detecting transient errors for variation learning and self-checking for asymmetric errors. From evaluations, the precompensation takes only 5/B time for batch operations of B cycles. Proposed BIST approach and method can reduced 2LN march tests to 6N for L-level RRAMs. The self-healing ability is verified by analog-Berger-code error detection. From experiments using a typical neural network for MNIST handwritten digit dataset the network can be healed with only 2% of accuracy and about 35% of training steps.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 27th Asian Test Symposium, ATS 2018
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781538694664
Publication statusPublished - 2018 Dec 6
Event27th IEEE Asian Test Symposium, ATS 2018 - Hefei, China
Duration: 2018 Oct 152018 Oct 18

Publication series

NameProceedings of the Asian Test Symposium
ISSN (Print)1081-7735


Other27th IEEE Asian Test Symposium, ATS 2018

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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