A memory yield improvement scheme combining built-in self-repair and error correction codes

Tze-Hsin Wu, Po-Yuan Chen, Mincent Lee, Bin Lin, Cheng-Wen Wu, Chen-Hung Tien, Hung-Chih Lin, Hao Chen, C. Peng, Min-Jer Wang
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引用次数: 24

Abstract

Error correction code (ECC) and built-in self-repair (BISR) schemes have been wildly used for improving the yield and reliability of memories. Many built-in redundancy-analysis (BIRA) algorithms and ECC schemes have been reported before. However, most of them focus on either BIRA algorithms or ECC schemes. In this paper, we propose an ECC-Enhanced Memory Repair (EEMR) scheme for yield improvement. Many modern memories are equipped with ECC in addition to BISR. We evaluate the back-end flow that combines both ECC and BIRA to determine whether yield can be improved by proper sequencing of the two steps. We also collect and identify important failure patterns and their distributions from over 100,000 sample memory instances, which are used to enhance the EEMR scheme that incorporates ECC. As ECC is failure pattern sensitive, careful evaluation from realistic failure bitmaps is necessary. We also verify the feasibility of implementing the proposed EEMR scheme by real test data. Experimental results from industrial 4Mb memory instances show that the proposed EEMR scheme gains over 2% instance yield on average, as compared with the traditional scheme. We also investigate the reliability of the EEMR scheme with different ECC specifications and BIRA algorithms.
一种结合内置自修复和纠错码的存储器良率改进方案
纠错码(ECC)和内置自修复(BISR)方案被广泛用于提高存储器的成品率和可靠性。许多内置冗余分析(BIRA)算法和ECC方案已经被报道过。然而,它们大多集中在BIRA算法或ECC方案上。在本文中,我们提出了一种ec - enhanced Memory Repair (EEMR)方案来提高良率。除了BISR外,许多现代存储器还配备了ECC。我们评估了结合ECC和BIRA的后端流程,以确定是否可以通过正确排序这两个步骤来提高产量。我们还从超过100,000个样本内存实例中收集和识别重要的故障模式及其分布,用于增强包含ECC的EEMR方案。由于ECC对故障模式敏感,因此有必要根据实际故障位图进行仔细评估。并通过实测数据验证了所提出的EEMR方案的可行性。在工业4Mb内存实例上的实验结果表明,与传统方案相比,提出的EEMR方案的实例产出率平均提高了2%以上。我们还研究了不同ECC规格和BIRA算法下EEMR方案的可靠性。
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