Memory Bad Block Allowance Modeling for Target QoS
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Solution Overview
Problem
Current methods for determining grown bad block allowance in memory devices during characterization are not aligned with performance metrics and degradation relationships, leading to inaccurate and inefficient settings that negatively impact Quality of Service (QoS).
Innovation Solution
A characterization process is implemented during a pre-runtime stage to establish grown bad block allowance based on relationships between parameters impacted by degradation over the lifecycle of the memory device, including throughput metrics and write amplification, to align with performance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grown bad block allowance is determined using current methods during characterization, then the setting process is simple, but the alignment with performance metrics and degradation relationships is poor, leading to inaccurate QoS settings
Solution Approach 1:
The patent applies preliminary action by determining the grown bad block allowance during the pre-runtime characterization stage rather than during runtime. The system performs degradation modeling and relationship analysis between parameters (such as program/erase cycle counts, throughput metrics, and write amplification) before the memory device is deployed, allowing accurate QoS settings to be established in advance based on predicted degradation patterns
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the grown bad block allowance based on determined relationships between multiple parameters including program/erase cycle counts, throughput metrics, and write amplification factors. The system modifies the allowance value according to the specific degradation characteristics and performance requirements of each memory device, rather than using fixed or simplified settings
2Productivity
If grown bad block allowance is set without considering degradation relationships, then the characterization process is fast, but the Quality of Service (QoS) performance deteriorates
Solution Approach 1:
The system performs degradation modeling and relationship determination during the pre-runtime characterization stage, establishing accurate grown bad block allowance settings before the memory device begins runtime operations. This preliminary analysis of degradation patterns ensures that QoS requirements are met throughout the device lifecycle without requiring slow runtime adjustments
Solution Approach 2:
The patent applies feedback by using determined relationships between parameters (program/erase cycles, throughput, write amplification) to continuously refine and adjust the grown bad block allowance. The system incorporates feedback from degradation modeling to optimize the allowance setting, ensuring that QoS performance is maintained while preserving characterization efficiency
Data Source
AI summary
A processing device determines, during a pre-runtime stage associated with a memory device, a first relationship comprising a first modeling of a first parameter of the memory device and a second parameter of the memory device. During the pre-runtime stage, a grown bad block allowance is determined based on the first relationship associated with the memory device. The grown bad blocks allowance is stored in a storage location associated with the memory device, where the memory device uses the grown bad blocks allowance during runtime.