Adaptive Program Verify for NAND Block Defect Detection
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Solution Overview
Problem
Existing memory devices face challenges in detecting subtle defects such as wordline leakage, leading to uncorrectable error correction code (UECC) errors, which compromise data integrity and reliability, especially in high-density non-volatile memory devices with reduced redundancy and error correction capabilities.
Innovation Solution
Implementing an adaptive program verify scheme with a sensitive verify operation, such as an in-field read operation, to detect low leakage currents and identify defective blocks, thereby avoiding UECC errors by incrementing failure byte counts and placing problematic blocks on an exclusion list.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional program verify operations are used, then defect detection capability is insufficient, but implementing comprehensive verify operations increases program operation time and energy consumption
Solution Approach 1:
The patent applies local quality by differentiating verify operations based on block characteristics. Blocks are categorized as high-risk or low-risk based on their defect profiles, and verify operations are selectively applied accordingly. High-risk blocks receive sensitive verify operations while low-risk blocks undergo standard verify operations, optimizing the balance between detection capability and operation time.
Solution Approach 2:
The patent implements dynamics through adaptive verify schemes that adjust verify intensity based on real-time block performance monitoring. The system dynamically transitions between standard and sensitive verify operations based on detected defect patterns, allowing the verify strategy to evolve with the block's operational history and risk profile.
2Reliability
If redundant array of independent NAND (RAIN) SRAM is used for error correction, then data reliability improves, but device complexity and energy consumption increase
Solution Approach 1:
The patent extracts the error detection function from the RAIN SRAM infrastructure and implements it through adaptive verify operations that identify defective blocks before they cause UECC errors. By detecting and isolating problematic blocks proactively, the system reduces reliance on RAIN SRAM for error correction, thereby reducing device complexity and energy consumption while maintaining reliability.
Solution Approach 2:
The patent applies preliminary action by performing defect detection through sensitive verify operations before actual data errors occur. By identifying and isolating defective blocks in advance, the system prevents UECC errors from happening, eliminating the need for reactive error correction mechanisms and reducing overall system complexity.
3Measurement precision
If sensitive verify operations are performed on all blocks, then defect detection accuracy improves, but energy consumption increases
Solution Approach 1:
The patent applies local quality by tailoring verify operation intensity to individual block characteristics. Blocks are assessed based on their defect histories and risk profiles, with sensitive verify operations reserved for high-risk blocks that require enhanced detection. Low-risk blocks undergo standard verify operations, significantly reducing overall energy consumption while maintaining high detection accuracy where needed.
Solution Approach 2:
The patent implements partial action by applying sensitive verify operations only to the subset of blocks that require them based on risk assessment, rather than uniformly across all blocks. This selective approach ensures adequate detection accuracy for problematic blocks while avoiding unnecessary energy expenditure on blocks that are unlikely to contain defects.
Data Source
AI summary
A system includes a memory device and a processing device, operatively coupled with the memory device, to perform operations including receiving a command to perform a program operation on a block of the memory device; determining whether the block is on an exclusion list; determining, in response to determining that the block is not on the exclusion list, whether a value of a media endurance metric of the block satisfies a media endurance metric criterion; performing, in response to determining that the value of the media endurance metric of the block satisfies the media endurance metric criterion, the program operation on the block; determining whether a media health metric of the block satisfies a health threshold criterion; and adding, in response to determining that the media health metric of the block satisfies the health threshold criterion, the block to the exclusion list.