AI Lifetime Estimation for Storage Devices
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Solution Overview
Problem
Current error correction technologies, such as BCH coding, struggle to keep pace with the rapid advancements in non-volatile memory manufacturing, leading to insufficient correction capabilities and reduced reliability, necessitating a more effective method to estimate the lifetime of storage devices.
Innovation Solution
A method is developed that involves training artificial intelligence using a neural network system to analyze operational parameters, decode bit values, classify memory units into different storage state regions, and determine the number of units in each region, thereby estimating the storage device's lifetime based on its operational performance and error correction capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If BCH coding technology is used for error correction, then computation speed is fast, but correction capability is insufficient for modern non-volatile memory
Solution Approach 1:
The patent transitions from BCH coding parameters to LDPC coding parameters, changing the fundamental error correction code parameters to achieve both fast computation and strong correction capability. The LDPC code parameters are optimized to maintain speed while improving correction capability for modern non-volatile memory devices.
2Reliability
If LDPC error correction technology is used, then correction capability is strong, but computational complexity increases
Solution Approach 1:
The LDPC decoding process is segmented into multiple stages including syndrome calculation, iterative decoding with check node processing and variable node processing, and termination conditions. This segmentation allows the complex decoding to be broken down into manageable operations that can be efficiently implemented in hardware while maintaining strong correction capability.
3Quantity of substance
If memory units undergo multiple erasing and writing cycles, then storage capacity is maintained, but error probability increases and reliability decreases
Solution Approach 1:
The patent implements preliminary error correction by classifying memory units into different states (strong correct, weak correct, strong error, weak error regions) before data writing operations. This preliminary classification allows the system to apply appropriate error correction strategies in advance, preventing error accumulation from multiple erasure and writing cycles while maintaining storage capacity.
Data Source
AI summary
The present disclosure provides a method of training artificial intelligence to estimate a lifetime of a storage device, which includes steps of: determining whether an operating parameter of the storage device executing a processing program on bit value values is smaller than an operational threshold parameter or not, if yes, decoding the bit value values stored in the storage device by a decoder; determining whether the bit value values stored in the storage device are successfully decoded by the decoder or not, if yes, classifying the memory unit of the storage device into a strong correct region, a weak correct region, a strong error region or a weak error region; determining whether the number of the memory units falls within an allowable number range or not, if not, initiating an artificial intelligence neural network system to use machine learning to estimate the lifetime of the storage device.