Characterizing and Optimizing LDPC Performance on 3D NAND Flash Memories

Author:

Li Qiao1ORCID,Chen Yu1ORCID,Wu Guanyu1ORCID,Du Yajuan2ORCID,Ye Min3ORCID,Gan Xinbiao4ORCID,Zhang Jie5ORCID,Shen Zhirong1ORCID,Shu Jiwu1ORCID,Xue Chun6ORCID

Affiliation:

1. School of Informatics, Xiamen University, Xiamen, China

2. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

3. YEESTOR Microelectronics Co., Ltd, Shenzhen, China

4. National University of Defense Technology, Changsha, China

5. The School of Computer Science, Peking University, Beijing, China

6. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

Abstract

With the development of NAND flash memories’ bit density and stacking technologies, while storage capacity keeps increasing, the issue of reliability becomes increasingly prominent. Low-density parity check (LDPC) code, as a robust error-correcting code, is extensively employed in flash memory. However, when the RBER is prohibitively high, LDPC decoding would introduce long latency. To study how LDPC performs on the latest 3D NAND flash memory, we conduct a comprehensive analysis of LDPC decoding performance using both the theoretically derived threshold voltage distribution model obtained through modeling (Modeling-based method) and the actual voltage distribution collected from on-chip data through testing (Ideal case). Based on LDPC decoding results under various interference conditions, we summarize four findings that can help us gain a better understanding of the characteristics of LDPC decoding in 3D NAND flash memory. Following our characterization, we identify the differences in LDPC decoding performance between the Modeling-based method and the Ideal case. Due to the accuracy of initial probability information, the threshold voltage distribution derived through modeling deviates by certain degrees from the actual threshold voltage distribution. This leads to a performance gap between using the threshold voltage distribution derived from the Modeling-based method and the actual distribution. By observing the abnormal behaviors in the decoding with the Modeling-based method, we introduce an Offsetted Read Voltage (ΔRV) method for optimizing LDPC decoding performance by offsetting the reading voltage in each layer of a flash block. The evaluation results show that our ΔRV method enhances the decoding performance of LDPC on the Modeling-based method by reducing the total number of sensing levels needed for LDPC decoding by 0.67% to 18.92% for different interference conditions on average, under the P/E cycles from 3,000 to 7,000.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Natural Science Foundation of Xiamen

Publisher

Association for Computing Machinery (ACM)

Reference58 articles.

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2. Yu Cai, Saugata Ghose, Yixin Luo, Ken Mai, Onur Mutlu, and Erich F. Haratsch. 2017. Vulnerabilities in MLC NAND flash memory programming: Experimental analysis, exploits, and mitigation techniques. In 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA’17). IEEE, 49–60.

3. Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai. 2013. Threshold voltage distribution in MLC NAND flash memory: Characterization, analysis, and modeling. In 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE’13). IEEE, 1285–1290.

4. Read Disturb Errors in MLC NAND Flash Memory: Characterization, Mitigation, and Recovery

5. Yu Cai, Yixin Luo, Erich F. Haratsch, Ken Mai, and Onur Mutlu. 2015. Data retention in MLC NAND flash memory: Characterization, optimization, and recovery. In 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA’15). IEEE, 551–563.

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