Gaussian Modeling for Soft-Read Threshold Estimation in NAND Flash
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Solution Overview
Problem
In solid state memory storage devices like NAND flash, noise from program disturb and inter-cell interference causes voltage distribution and level changes over time, affecting data integrity and longevity, which existing technologies fail to adequately address.
Innovation Solution
The use of Gaussian modeling to estimate soft-read thresholds in memory devices, assuming distinct means and standard deviations for each cell voltage level distribution, improves the accuracy of read voltages and enhances memory performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gaussian modeling with distinct means and standard deviations is used for each PV state, then read voltage accuracy and data integrity are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing Gaussian model parameters (means and standard deviations) for each PV state during manufacturing or initial characterization. These pre-computed parameters are then used during normal read operations without requiring real-time complex calculations, thus achieving high read voltage accuracy while minimizing processing complexity during operation.
Solution Approach 2:
The patent uses copying by creating simplified representations of the complex voltage distributions through Gaussian models. Instead of dealing with the full complexity of actual cell voltage distributions, the patent copies the essential characteristics (mean and standard deviation) into Gaussian model parameters that can be efficiently used for threshold estimation and read voltage determination.
2Measurement precision
If multiple Gaussian models with distinct parameters are generated for each PV state, then threshold estimation accuracy is improved, but memory resource consumption increases
Solution Approach 1:
The patent applies local quality by assigning specific Gaussian model parameters (distinct means and standard deviations) to each individual PV state rather than using a single global model. This allows the system to capture the unique characteristics of each PV state's voltage distribution, improving threshold estimation accuracy for each state while only storing the necessary parameters for that specific state.
Solution Approach 2:
The patent uses parameter changes by varying the Gaussian model parameters (mean and standard deviation) for each PV state to reflect the actual voltage distribution characteristics. By adjusting these parameters to match each PV state's specific distribution, the system achieves accurate threshold estimation without requiring excessive memory resources, as only two parameters per state are needed rather than storing complete distribution data.
Data Source
AI summary
Devices, systems and methods for improving the performance of a memory device are described. An example method includes obtaining a plurality of cell counts for each of a plurality of read voltages applied to the memory device, generating, based on the plurality of cell counts, a set of Gaussian models for a plurality of PV states corresponding to the plurality of read voltages, each of the set of Gaussian models comprising a mean parameter and a standard deviation parameter, determining, based on the set of Gaussian models, the mean parameter and the standard deviation parameter for each of the plurality of PV states, determining, based on the mean parameter and the standard deviation parameter for each of the plurality of PV states, a plurality of updated read voltages, and applying the plurality of updated read voltages to the memory device to retrieve information from the memory device.