SSD Temperature Sensor Failure Estimation and Mitigation
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Solution Overview
Problem
Data storage devices, such as SSDs, face performance issues and risk of data loss due to excess heat when temperature sensors fail, as they lack effective methods to accurately estimate temperatures at failed sensors, leading to potential thermal protection failures and data loss.
Innovation Solution
A data storage device equipped with a processor that detects failed temperature sensors, estimates temperatures using data from other sensors through predetermined formulas or machine learning calibration, and controls operations based on these estimates to prevent thermal overload and data loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temperature sensors are installed in SSDs to measure operating temperatures, then thermal protection is improved, but the system becomes vulnerable to sensor failures that can cause data loss
Solution Approach 1:
The system performs preliminary calibration during manufacturing to establish relationships between temperatures at different sensor locations. This advance preparation enables the system to predict temperatures at failed sensor locations using data from functioning sensors, thereby maintaining thermal protection even when sensors fail.
Solution Approach 2:
The system uses temperature data from functioning sensors as an intermediary to infer temperatures at failed sensor locations. By establishing mathematical relationships between sensor locations during calibration, the system can use readings from working sensors to estimate conditions at failed sensors, maintaining thermal protection without requiring all sensors to be operational.
2Measurement precision
If multiple temperature sensors are used to monitor SSD temperature, then thermal management accuracy is improved, but the complexity of detecting and responding to sensor failures increases
Solution Approach 1:
The system continuously monitors temperature sensor readings and compares them against expected relationships established during calibration. When a sensor failure is detected through this feedback mechanism, the system automatically switches to using data from functioning sensors to estimate temperatures, providing a straightforward response that reduces operational complexity despite the initial detection challenge.
3Reliability
If the SSD switches to Read Only mode upon sensor failure to ensure safety, then data protection is improved, but performance and data accessibility deteriorate
Solution Approach 1:
The system creates a virtual representation of failed sensor data by estimating temperatures at failed sensor locations using mathematical relationships and data from functioning sensors. This virtual temperature data allows the system to maintain thermal protection controls without switching to Read Only mode, preserving full write performance while ensuring safety through accurate temperature monitoring.
Data Source
AI summary
Methods and apparatus for detecting a failed temperature sensor within a data storage device and for mitigating the loss of the sensor are provided. One such data storage device includes a non-volatile memory (NVM), a set of temperature sensors, and a processor coupled to the NVM and the temperature sensors. The processor is configured to detect failure of one of the temperature sensors and obtain temperature data from the other temperature sensors. The processor is further configured to estimate, based on the obtained temperature data, the temperature at the failed sensor, and then control at least one function of the data storage device based on the estimated temperature, such as controlling entry into a Read Only mode. In some examples, the processor estimates the temperature at the failed sensor or at various virtual sensor locations using pre-determined formulas having offsets and coefficients determined during an initial machine learning calibration procedure.