Proceedings Article10.1109/PACRIM.2009.5291247
Optimized cell programming for flash memories
Anxiao Jiang,Hao Li +1 more
- 23 Oct 2009
- pp 914-919
16
TL;DR: This paper presents an effective algorithm for finding the optimal programming strategy that optimizes the expected precision in flash memory cells and considers two metrics that are suitable for the multi-level cell technology and the rank modulation technology.
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Abstract: Flash memory cells use the charge they store to represent data. The amount of charge injected into a cell is called the cell's level. Programming a cell is the process of increasing a cell's level to the target value via charge injection, and the storage capacity of flash memories is limited by the precision of cell programming. To optimize the precision of the final cell level, a cell is programmed adaptively with multiple rounds of charge injection. Due to the high cost of block erasure, when cells are programmed, their levels are only allowed to increase. Such a storage medium can be modelled by a Write Asymmetric Memory model. It is interesting to study how well such storage media can be programmed. In this paper, we focus on the programming strategy that optimizes the expected precision. The performance criteria considered here include two metrics that are suitable for the multi-level cell technology and the rank modulation technology, respectively. Assuming that the charge-injection noise has a uniform random distribution, we present an effective algorithm for finding the optimal programming strategy. The optimal strategy can be used to program cells efficiently.
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Citations
Read and Write Voltage Signal Optimization for Multi-Level-Cell (MLC) NAND Flash Memory
TL;DR: The proposed write and read voltage optimization schemes not only minimize the error probability throughout the operational lifetime of flash memory, but also improve the decoding convergence speed.
Coding for Unreliable Flash Memory Cells
TL;DR: This work shows that memory cells can be broadly categorized into reliable and unreliable cells, and suggests a coding scheme, using generalized tensor product codes, that programs the unreliable cells only at certain voltage levels that are less likely to result in errors.
18
Parallel programming of rank modulation
Minghai Qin,Anxiao Andrew Jiang,Paul H. Siegel +2 more
- 07 Jul 2013
TL;DR: This paper derives upper bounds on the minimum number of programming rounds required to achieve cell-level vector ℓ(τ), denoted by t1(τ,ℓ< sub>0), and proposes a programming algorithm for which the resultant number ofProgramming rounds is close to t-sub-2*(τ, ™0), which maximizes the number of information update cycles supported by the device before requiring a block erasure.
13
On the parallel programming of flash memory cells
Eitan Yaakobi,Anxiao Jiang,Paul H. Siegel,Alexander Vardy,Jack K. Wolf +4 more
- 30 Sep 2010
TL;DR: This paper studies the parallel programming of flash memory cells, and presents algorithms for parallel programming when there is information on the cells' hardness for charge injection, but there is no feedback information on cell levels during programming.
Optimized cell programming for flash memories with quantizers
Minghai Qin,Eitan Yaakobi,Paul H. Siegel +2 more
- 01 Jul 2012
TL;DR: A new criterion is proposed to evaluate the performance of the cell programming which is more suitable for flash memories in practice and then the parallel programming strategy is optimized accordingly to achieve the optimal performance.
References
Rank Modulation for Flash Memories
TL;DR: A novel data representation scheme for multilevel flash memory cells, in which a set of n cells stores information in the permutation induced by the different charge levels of the individual cells, which eliminates the need for discrete cell levels when programming cells.
•Book
Flash Memories
Paulo Cappelletti,Carla Golla +1 more
- 01 Jun 1999
TL;DR: In this paper, the authors provide a comprehensive information on basic memory cell structures, device physics and technology, simulation circuit architecture, system issues, testing and reliability, and applications of flash memories.
317
Error-correcting codes for rank modulation
Anxiao Jiang,Moshe Schwartz,Jehoshua Bruck +2 more
- 20 Nov 2008
TL;DR: It is shown that the adjacency graph of permutations is a subgraph of a multi-dimensional array of a special size, a property that enables code designs based on Lee- metric codes.
Joint coding for flash memory storage
Anxiao Jiang,Jehoshua Bruck +1 more
- 06 Jul 2008
TL;DR: This paper presents several new floating code constructions, which include both codes with specific parameters and general code Constructions that are asymptotically optimal and bounds to the performance of floating codes.
Designing floating codes for expected performance
Flavio Chierichetti,Hilary K. Finucane,Zhenming Liu,Michael Mitzenmacher +3 more
- 01 Sep 2008
TL;DR: It is demonstrated that codes designed for expected performance can differ substantially from optimal worst-case codes, and constructions for some simple cases are suggested, focusing on the issue of expected behavior.