NAND Flash Soft-Read FER Estimation Using Mutual Information
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Solution Overview
Problem
Determining the Frame Error Rate (FER) in NAND flash non-volatile memory is a time-consuming task, requiring millions of read and write operations, which is inefficient and resource-intensive, especially when selecting optimal error correction codes for memory devices in systems like Solid State Drives.
Innovation Solution
The method involves using a system with a memory controller that determines FER through a soft read process, generating joint probability values, and employing an MI-FER conversion data structure to estimate FER using mutual information, allowing for accurate estimation with significantly fewer read processes, potentially just one to ten, compared to millions in traditional systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error rate estimation methods are used, then accurate FER estimation can be achieved, but the process requires millions of read and write operations which is time-consuming and resource-intensive
Solution Approach 1:
The patent transforms the FER estimation problem from direct error counting to mutual information calculation. By changing the measurement parameter from binary read outcomes to soft read confidence values, the system achieves accurate FER estimation through fewer operations. The mutual information metric captures channel reliability without requiring exhaustive error sampling.
Solution Approach 2:
The patent replaces the mechanical process of performing millions of read/write operations with a computational approach using soft read processes and mutual information calculation. Instead of physically stress-testing the memory through repeated operations, the system uses probabilistic modeling and information theory to estimate error rates.
2Measurement precision
If traditional error rate estimation methods are used, then accurate FER estimation can be achieved, but the process requires millions of read and write operations which is resource-intensive
Solution Approach 1:
The patent changes the operational parameter from hard read/write cycles to soft read processes with confidence metric collection. This parameter change enables the system to gather necessary statistical information without the overhead of full write-verify cycles, dramatically improving operational efficiency while maintaining estimation accuracy.
Solution Approach 2:
The patent extracts only the essential information needed for FER estimation—the mutual information between input and output channels—without performing the complete write-verify cycle. By taking out just the critical measurement component and eliminating redundant operations, the system achieves high productivity.
3Reliability
If strong error correction codes are selected to ensure data integrity, then error protection is improved, but computing resources are wasted on overly strong correction
Solution Approach 1:
The patent implements dynamic ECC selection based on real-time mutual information measurements. Instead of using a fixed strong ECC for all conditions, the system adapts the error correction strength to the actual channel quality, consuming computing resources only when necessary to achieve the desired reliability level.
Solution Approach 2:
The patent uses mutual information measurement as feedback to guide ECC code selection. The measured channel quality informs the choice of appropriate error correction strength, ensuring that computing resources are allocated efficiently—applying strong ECC only when the channel conditions warrant it.
Data Source
AI summary
A method for fast calculation of a frame error rate (FER) of an error correcting code (ECC) soft decoder using a soft read process includes determining an MI-FER conversion data structure based on a relationship between mutual information (MI) of input channels and output channels of a memory, and FER of the ECC soft decoder, and decoding an encoded data codeword stored in a memory page of the memory and read using a soft read process. The method further includes generating a set of joint probability values using the information from the soft read process and data indicating true bit values for the data codeword, determining an MI value using the set of joint probability values, and determining an FER estimate using the MI-FER conversion data structure.