Group Classification for Solid State Storage ICI Mitigation
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Solution Overview
Problem
In solid state storage devices, particularly NAND-based flash memories, the variation in threshold voltage distribution among cells leads to misjudgment of storage states during the read cycle, resulting in increased data error rates due to inter-cell interference (ICI) effects, which complicates the classification process and consumes significant computational resources.
Innovation Solution
A group classification method is introduced that establishes a voltage shift parameter table to estimate threshold voltage shifts caused by ICI patterns, allowing central cells to be classified into groups based on these shifts, thereby reducing data error rates and simplifying the classification process by using fewer ICI patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cells are classified into multiple storing states based on threshold voltage distribution, then data storage capacity is improved, but threshold voltage variation causes misjudgment of storing states during read cycle
Solution Approach 1:
The patent performs preliminary classification of central cells into groups based on ICI patterns before the read cycle. By pre-identifying which central cells are affected by inter-cell interference and grouping them accordingly, the system prepares compensation strategies in advance, allowing for more accurate storing state judgment during the read cycle despite threshold voltage variations.
Solution Approach 2:
The patent changes the parameter used for classification from simple threshold voltage to a combination of ICI patterns and threshold voltage shifts. By considering the pattern of neighboring cell states (ICI patterns) and how they affect central cell threshold voltages, the system creates a more robust classification that accounts for voltage variations, thereby improving measurement precision in storing state judgment.
2Measurement precision
If all ICI patterns are considered for classification, then classification accuracy is improved, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent extracts only the significant ICI patterns that have a notable impact on central cell threshold voltage shifts, rather than considering all possible ICI patterns. By identifying and focusing on the most influential patterns, the system maintains classification accuracy while significantly reducing the computational burden and resource consumption associated with analyzing every possible neighboring cell configuration.
Data Source
AI summary
A group classification method includes the following steps. Firstly, a voltage shift parameter table is established. The voltage shift parameter table includes a first positional parameter table corresponding to a first neighboring cell. Then, MN ICI patterns are determined according to N neighboring cells having a significant ICI effect. If the central cell has a first storing state, MN central cell threshold voltage shifts corresponding to the MN ICI patterns are determined according to the voltage shift parameter table, and the first storing state is divided into plural sub-regions. Afterwards, the central cells corresponding to a first number of ICI patterns are classified into a first group of the first storing state. The central cell threshold voltage shifts corresponding to the first number of ICI patterns lie in a first sub-region of the first storing state.