Memory Block Retirement Using BER Thresholds and Partial Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing memory systems face challenges in accurately determining when to retire memory blocks to avoid premature or belated retirement, leading to performance issues such as increased read latency and data loss.
Innovation Solution
Implementing a memory system that employs multiple levels of error correction, including RAIN error correction, to assess bit error rates and increment counters for defective blocks, allowing precise retirement of entire or partial blocks based on threshold comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory blocks are retired early to avoid defects, then reliability is improved, but productivity deteriorates due to loss of usable storage capacity
Solution Approach 1:
The patent replaces traditional mechanical threshold-based retirement mechanisms with a machine learning model that dynamically assesses block health. The ML model analyzes multiple parameters including read latency, programming failures, and error correction rates to predict future block performance, enabling more accurate retirement decisions that balance reliability and capacity utilization.
Solution Approach 2:
The system dynamically adjusts retirement parameters based on changing memory characteristics over time. Instead of fixed thresholds, the ML model continuously learns from operational data and adapts retirement criteria to account for wear patterns, temperature variations, and workload characteristics, optimizing the balance between early retirement (reliability) and extended usage (productivity).
2Productivity
If memory blocks are used longer to maximize capacity, then productivity is improved, but reliability deteriorates due to increased risk of defects
Solution Approach 1:
The system implements continuous feedback monitoring of memory block health through multiple metrics including read latency measurements, programming failure rates, and error correction code performance. This feedback loop provides real-time information to the ML model, enabling proactive retirement decisions before defects manifest, thus maintaining both high productivity and reliability.
Solution Approach 2:
The ML model performs preliminary assessment of block health trends before critical failures occur. By analyzing progressive degradation patterns in read latency and error rates, the system can predict future failures and schedule retirement operations in advance, preventing data loss while maximizing productive usage of the memory blocks.
3Device complexity
If traditional error correction methods are used, then device complexity is reduced, but measurement precision deteriorates in detecting defective blocks
Solution Approach 1:
The patent segments the error correction and detection function into multiple specialized components: traditional ECC for basic error correction, read latency measurement mechanisms for performance monitoring, programming failure tracking, and a separate ML model for predictive analysis. This segmentation allows each component to specialize in its function, improving overall detection precision without requiring a complete redesign of the entire system.
Solution Approach 2:
The ML model acts as an intermediary layer between traditional error correction mechanisms and the retirement decision-making process. It synthesizes data from multiple sources including ECC performance, read latency measurements, and programming statistics to provide refined predictions about block health, thereby enhancing detection accuracy while maintaining the simplicity of existing error correction infrastructure.
Data Source
AI summary
Methods, systems, and devices for detection and retirement of defective blocks are described. Techniques described herein may enable a memory system to determine if a block of memory cells may be partially retired. For example, the memory system may determine if an error correction counter has satisfied a first threshold or if a bit error rate (BER) of the block of memory cells satisfies a second threshold. The memory system may determine if a BER of one or more word lines of a first deck of the block of memory cells and a BER of one or more word lines of neighboring decks of the block of memory cells satisfy respective thresholds. The memory system may accordingly determine whether to refrain from retiring the block of memory cells, to partially retire the block of memory cells, or to fully retire the block of memory cells.