Algorithm Qualifier Commands for Memory Retention Without Added Latency
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Solution Overview
Problem
Existing memory devices face challenges in maintaining accurate data retention and operational efficiency due to threshold voltage shifts caused by varying configurations, cell types, architectures, workload, and environmental conditions, leading to decreased performance over time.
Innovation Solution
Implementing algorithm qualifier commands that allow for selecting and adjusting trim settings, such as half good block selections, coarse threshold estimate reads, and on-the-fly soft bit management, to optimize memory device operations based on specific conditions and requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trim settings are adjusted to maintain accurate data retention, then data retention is improved, but device complexity increases
Solution Approach 1:
The memory device automatically executes algorithm qualifier commands and adjusts trim settings based on detected operational conditions without requiring external intervention. The device self-monitors threshold voltage shifts and self-corrects by selecting appropriate trim settings from stored algorithms, enabling autonomous maintenance of data retention while avoiding the complexity of external control systems.
Solution Approach 2:
Multiple algorithm options with different trim settings are pre-configured and stored in the memory device before operation. These pre-prepared algorithms account for various operational conditions and threshold voltage shifts, allowing the device to quickly select and apply the appropriate algorithm without real-time computation, thereby maintaining data retention without adding operational complexity.
2Productivity
If algorithm options are selected to optimize performance, then productivity is improved, but device complexity increases
Solution Approach 1:
The memory device dynamically selects from multiple stored algorithm options based on current operational conditions such as workload type, temperature, and detected performance degradation. This dynamic adaptation allows the device to optimize productivity for different scenarios without requiring a fixed complex configuration, as the selection logic follows simple condition-based rules rather than complex real-time optimization algorithms.
Solution Approach 2:
Different algorithm options correspond to different parameter configurations including trim settings, voltage levels, and timing parameters. By changing these parameters based on operational conditions rather than redesigning the device architecture, the system achieves varied performance optimization while maintaining relatively simple device structure.
3Adaptability or versatility
If trim settings are adjusted dynamically, then adaptability is improved, but loss of time increases
Solution Approach 1:
Multiple algorithm options with different trim settings are pre-configured and stored in the memory device before operation. These pre-prepared algorithms account for various operational conditions and threshold voltage shifts, allowing the device to quickly select and apply the appropriate algorithm without real-time computation, thereby maintaining data retention without adding operational complexity.
Data Source
AI summary
Apparatuses, systems, and methods for algorithm qualifier commands are described according to embodiments of the present disclosure. One example method can include executing an algorithm qualifier command on a memory device and performing an operation on the memory device for a command sequence that follows the algorithm qualifier command using a number of settings indicated by the algorithm qualifier command. The algorithm qualifier command can indicate a number of settings to use while performing the operation on the memory device.