Row-Level NAND Flash Programming for BER and Wear Reduction
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Solution Overview
Problem
NAND flash memory devices experience increased bit-error-rate (BER) due to charge traps and retention effects, particularly in high-density structures like QLC and PLC, requiring improved programming and read methods to ensure performance and reliability.
Innovation Solution
A system and method for determining optimal voltage thresholds (VTs) and program parameters based on production information and row-specific adjustments, including dynamic adaptation of program voltage windows and read bias levels to minimize wear and reduce BER.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple-stage programming is used to achieve accurate state programming with low standard-deviation of voltage thresholds, then manufacturing precision is improved, but programming time increases
Solution Approach 1:
The system performs preliminary determination of optimal program parameters based on production information from a sample set before actual programming operations. This pre-characterization allows the system to have parameter optimization ready in advance, eliminating the need for time-consuming multiple-stage programming while maintaining accurate state programming.
Solution Approach 2:
The system changes program parameters dynamically based on production information and row-specific characteristics. By determining optimal parameters in advance from sample data and applying them to production, the system achieves accurate programming with reduced time compared to traditional multiple-stage approaches.
2Reliability
If row-specific program parameters are determined and adjusted to optimize performance, then reliability is improved, but device complexity increases
Solution Approach 1:
The system segments the memory device into multiple rows and determines optimal program parameters for each row independently based on production information. This segmentation allows targeted optimization for each row while using automated processes to manage the complexity of handling multiple rows with different parameters.
Solution Approach 2:
The system uses production information from sample sets as feedback to determine optimal program parameters. This feedback mechanism automates the parameter optimization process, improving reliability through data-driven decisions while reducing the manual complexity of managing row-specific parameters.
3Productivity
If production information from sample sets is used to determine optimal parameters, then productivity is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary measurements and characterizations on a sample set of memory devices to determine optimal program parameters before production programming. This preliminary action captures necessary production information in advance, enabling high-speed production programming while the measurement precision requirements are confined to the initial sample characterization phase.
Solution Approach 2:
The system uses production information from a sample set as a template or copy that represents the characteristics of the entire production batch. By measuring a representative sample with high precision and copying those parameter optimizations to production, the system achieves high productivity while concentrating measurement precision requirements on the smaller sample set rather than every individual device.
Data Source
AI summary
One or more processors of a system may determine one or more program parameters of a plurality of rows of cells of a non-volatile memory, determine a first threshold of a first parameter of each row based on the one or more program parameters of the plurality of rows, determine, by changing a programming time of each row, a set of parameters of each row that causes the first parameter not to cross the first threshold, determine a second threshold of a second parameter of each row based on production statistics of a plurality of dies as a result of mass production, adjust the programming time of each row to cause the second parameter not to cross the second threshold, and program data to a first row of the plurality of rows using the set of parameters of the first row and the adjusted programming time of the first row.