DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Moon, Jaekyun | - |
dc.contributor.advisor | 문재균 | - |
dc.contributor.author | Jo, Sunyoung | - |
dc.date.accessioned | 2023-06-23T19:33:46Z | - |
dc.date.available | 2023-06-23T19:33:46Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1030528&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309112 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[v, 51 p. :] | - |
dc.description.abstract | The NAND flash, which is used as a storage device in various application fields, it is essential to improve the performance of solution algorithms for high memory reliability. As the memory density increases, changes in physical characteristics and distortion of threshold voltage distribution occur, making it increasingly difficult to accurately read stored data bits from memory. In accordance with the development of deep learning technology, we aim to improve the reliability of memory by applying an AI algorithm in the process of responding to threshold voltage distribution deterioration. This paper proposes a deep learning-based read bias voltage estimation algorithm to accurately read data bits stored in NAND flash in response to the threshold voltage distribution distortion. The proposed algorithm has a two-stage decision structure that determines the read bias voltage after reconstructing the threshold voltage distribution based on sparse read trial information. After extracting the characteristics of the distribution that can express the shape of the entire distribution, we proposed a method of restoring the distribution by combining it with the read trial information. In addition, it was confirmed that the proposed algorithm well exceeds the performance of conventional non-learning techniques using datasets collected from actual NAND. It is expected that the proposed algorithms, which predict read bias voltages quickly and accurately with only a few read trials, can significantly improve memory reliability when applied to actual NAND systems. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | TLC NAND read bias decision▼aVth distribution▼aSparse data learning▼aDistribution reconstruction▼aDistribution feature extraction▼aNon-negative matrix factorization▼aVariational autoencoder | - |
dc.subject | TLC 낸드 읽기 전압 결정▼a문턱전압분포▼a희소 데이터 학습▼a분포 복원▼a분포 특성 추출▼a비음수 행렬 분해▼a변분 오토인코더 | - |
dc.title | Deep learning for NAND read bias decision | - |
dc.title.alternative | 극소수 읽기 시도 기반 최적 낸드 읽기 전압 결정 인공지능 기법 연구 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 조선영 | - |
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