A built-in self-diagnosis and repair design with fail pattern identification for memories

Chin Lung Su, Rei Fu Huang, Cheng Wen Wu, Kun Lun Luo, Wen Ching Wu

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

With the advent of deep-submicrometer VLSI technology, the capacity and performance of semiconductor memory chips is increasing drastically. This advantage also makes it harder to maintain good yield. Diagnostics and redundancy repair methodologies thus are getting more and more important for memories, including embedded ones that are popular in system chips. In this paper, we propose an efficient memory diagnosis and repair scheme based on fail-pattern identification. The proposed diagnosis scheme can distinguish among row, column, and word faults, and subsequently apply the Huffman compression method for fault syndrome compression. This approach reduces the amount of data that need to be transmitted from the chip under test to the automatic test equipment (ATE) without losing fault information. It also simplifies the analysis that has to be performed on the ATE. The proposed redundancy repair scheme is assisted by fail-pattern identification approach and a flexible redundancy structure. The area overhead for our built-in self-repair (BISR) design is reasonable. Our repair scheme uses less redundancy than other redundancy schemes under the same repair rate requirement. Experimental results show that the area overhead of the BISR design is only 4.1% for an 8 K $\times 64$ memory and is in inverse proportion to the memory size.

Original languageEnglish
Article number5593911
Pages (from-to)2184-2194
Number of pages11
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume19
Issue number12
DOIs
Publication statusPublished - 2011 Dec 1

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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