Dynamic Data Scrambling for Memory Wear Balancing
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Solution Overview
Problem
Existing data storage systems in memory devices often fail to adequately scramble data, leading to insufficient randomness, which can result in severe interference and uneven wear across memory cells, particularly in cases where data is not inherently random or is padded with zeros.
Innovation Solution
A method for data storage that involves scrambling data using a given seed, assessing the statistical distribution of the scrambled data, and modifying the scrambling configuration to improve randomness, including techniques such as adding dummy bits, using multiple scrambling seeds, and balancing bit values across memory cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored without scrambling or with simple scrambling, then storage speed and simplicity are improved, but data randomness is insufficient leading to severe interference and uneven wear across memory cells
Solution Approach 1:
The patent implements dynamic scrambling configuration that adapts based on data characteristics. The system assesses the statistical distribution of data and modifies scrambling parameters accordingly, transitioning from static to dynamic scrambling approaches. This allows the scrambling mechanism to respond to actual data patterns, improving randomness where needed while maintaining simplicity for already-random data.
Solution Approach 2:
The patent changes scrambling parameters such as seed selection and scrambling configuration based on data assessment. By monitoring statistical properties of the data and adjusting scrambling parameters dynamically, the system achieves improved data randomness without requiring a completely complex scrambling architecture, thus resolving the contradiction between reliability and device complexity.
2Object-affected harmful factors
If scrambling configuration is modified to improve randomness, then data interference is reduced, but processing time and computational overhead increase
Solution Approach 1:
The patent applies partial scrambling modification by assessing data characteristics first and only modifying scrambling configuration when necessary. Rather than always applying complex scrambling, the system performs partial assessments and applies enhanced scrambling only when data patterns indicate potential interference issues, thus reducing overall processing time while still mitigating harmful interference when needed.
Solution Approach 2:
The patent implements feedback mechanisms where the scrambling system continuously assesses data statistical distribution and adjusts scrambling configuration accordingly. This closed-loop approach allows the system to detect when interference is likely and respond with appropriate scrambling modifications, minimizing unnecessary processing while ensuring interference reduction when actually needed.
3Reliability
If multiple scrambling seeds are used to enhance randomness, then wear balancing across memory cells is improved, but device complexity and seed management overhead increase
Solution Approach 1:
The patent segments the scrambling seed management into multiple seeds that are selectively applied to different data blocks or memory regions. By dividing the scrambling operation into segments with different seeds, the system achieves better wear balancing across memory cells while managing complexity through organized seed selection strategies rather than uncontrolled multiple seed usage.
Solution Approach 2:
The patent creates a universal seed management system that handles multiple scrambling seeds through a unified assessment and selection framework. The same assessment mechanism evaluates data characteristics and selects appropriate seeds from a pool, making the multi-seed system as manageable as a single-seed system while achieving superior wear balancing through strategic seed selection.
4Reliability
If data is assessed and scrambling configuration is modified dynamically, then storage reliability is improved, but processing overhead and computational resources increase
Solution Approach 1:
The patent applies partial assessment by evaluating key statistical properties of data rather than进行全面 analysis. The system performs selective assessments focusing on critical randomness indicators and only modifies scrambling configuration when assessment results indicate genuine reliability issues, thus reducing processing overhead while maintaining improved storage reliability where actually needed.
Data Source
AI summary
A method for data storage includes scrambling data for storage in a memory device using a given scrambling seed. A statistical distribution of the scrambled data is assessed, and a measure of randomness of the statistical distribution is computed. A scrambling configuration of the data is modified responsively to the measure of randomness, and the data having the modified scrambling configuration is stored in the memory device.