Adaptive Read Scrub With Dynamic BER Threshold Relocation
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Solution Overview
Problem
Data storage devices like SSDs face challenges in data eviction due to abnormal environmental conditions, leading to potential data loss, performance degradation, and increased write amplification, as they struggle to differentiate between innate bit error rates and errors caused by environmental factors.
Innovation Solution
A controller in the data storage device collects environment data including temperature, program/erase cycles, and past relocations, dynamically adjusts bit error rate thresholds, and decides on data relocation based on this information to optimize data eviction policies and reduce unnecessary relocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the data storage device evicts data based on high BER notifications to prevent data loss, then data reliability is improved, but device performance deteriorates due to visible evictions and write amplification
Solution Approach 1:
The patent changes the parameter of BER threshold from a fixed value to a dynamic value that adapts based on environmental conditions (temperature, humidity) and device state (program/erase cycles, data age). This allows the system to distinguish between innate high BER and environmentally-induced BER, reducing unnecessary evictions while maintaining data reliability.
Solution Approach 2:
The system implements feedback by continuously monitoring environmental conditions and device state, then adjusting the BER threshold accordingly. This closed-loop approach enables the system to learn from past evictions and environmental patterns, optimizing the balance between data reliability and performance over time.
2Reliability
If the data storage device verifies each page or wordline to determine innate BER, then data reliability is improved, but device complexity increases and performance deteriorates
Solution Approach 1:
The patent extracts the verification process from a page-level or wordline-level operation and elevates it to a block-level operation. By verifying at the block level and using environmental context to infer page-level BER characteristics, the system reduces verification complexity while maintaining reliability.
Solution Approach 2:
The system performs preliminary verification at the block level before committing to page-level evictions. Environmental condition data is collected and analyzed in advance to predict which pages are likely to have innate high BER, allowing the system to focus verification resources only on suspicious blocks rather than verifying every page.
3Reliability
If the data storage device performs frequent data relocations to prevent data loss, then data reliability is improved, but device endurance deteriorates due to increased write amplification
Solution Approach 1:
The patent changes the relocation decision parameter from a simple BER threshold comparison to a multi-factor assessment including environmental conditions, program/erase cycle count, data age, and historical eviction patterns. This refined parameter set reduces false positives and unnecessary relocations, extending device endurance while maintaining reliability.
Solution Approach 2:
The system implements dynamic relocation policies that adapt to current environmental conditions and device state. Rather than performing fixed-schedule relocations or relocating based on static thresholds, the system dynamically adjusts relocation frequency and target selection based on real-time monitoring of temperature, humidity, and wear indicators.
4Ease of operation
If the data storage device uses a fixed BER threshold for data eviction, then ease of operation is improved, but adaptability deteriorates under varying environmental conditions
Solution Approach 1:
The patent transforms the static BER threshold into a dynamic parameter that automatically adapts to environmental conditions. The threshold adjusts based on temperature, humidity, program/erase cycles, and data age, allowing the system to maintain optimal eviction behavior across varying conditions without requiring manual reconfiguration.
Solution Approach 2:
The system performs self-adjustment of the BER threshold based on monitored environmental conditions and device state. Rather than requiring external configuration or manual tuning, the system automatically learns from environmental patterns and adjusts its eviction policy accordingly, maintaining both simplicity and adaptability.
Data Source
AI summary
A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to receive a read command form a host device, collect environment data of the memory device, decode data associated with the read command, determine a bit error rate (BER) of the decoded data, compare the BER to a threshold, and determine whether the data associated with the read command is to be relocated. The environment data includes temperature, number of program/erase cycles, amount of grown defects, number of past relocations and time since last data relocation. The controller is further configured to dynamically adjust the threshold based on the collected environment data and an amount of time that has passed since a last relocation of the read command data.