PCB Thermal Sensor Clustering for Early Heat Source Detection
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Solution Overview
Problem
Conventional thermal sensor placement in computing devices is often manually determined, leading to inaccurate temperature readings and potential system issues due to unanticipated heat sources, which are not detected during traditional design and validation processes.
Innovation Solution
A weighted thermal sensor clustering system using machine learning to optimize the placement of heat sources and thermal sensors through new design clustering methods, integrating a K-means classifier to identify optimal heat source clusters and their corresponding sensor locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual thermal sensor placement is used, then device complexity is reduced, but measurement precision deteriorates due to inaccurate temperature readings
Solution Approach 1:
The patent replaces manual mechanical placement of thermal sensors with an automated machine learning system that uses K-means clustering algorithms to determine optimal sensor locations. This substitution transforms the manual process into an automated computational approach, improving measurement precision while managing complexity through algorithmic optimization.
Solution Approach 2:
The system changes the approach from fixed manual placement to dynamic algorithmic determination of sensor positions. By using machine learning models that analyze component location data and thermal characteristics, the system optimizes sensor placement parameters based on actual thermal patterns, thereby improving temperature reading accuracy.
2Reliability
If traditional design validation processes are used, then ease of operation is maintained, but reliability deteriorates due to undetected unanticipated heat sources
Solution Approach 1:
The patent implements preliminary thermal analysis during the design phase using machine learning models to predict potential heat sources and thermal issues before the system is built. This preliminary action identifies problematic areas in advance, allowing designers to address reliability concerns during design rather than discovering them during operation or testing.
Solution Approach 2:
The system incorporates feedback loops where machine learning models continuously analyze component location files, thermal data, and system performance to refine sensor placement recommendations. This feedback mechanism improves reliability by iteratively optimizing the thermal management design based on predicted thermal patterns and actual performance data.
3Loss of information
If conventional thermal sensor placement is used, then manufacturing precision requirements are reduced, but loss of information increases due to undetected thermal issues
Solution Approach 1:
The patent replaces conventional trial-and-error sensor placement methods with machine learning-based optimization algorithms. This substitution reduces the need for high manufacturing precision in manual placement by providing algorithmically determined optimal locations that can be consistently reproduced, thereby reducing information loss about thermal conditions.
Data Source
AI summary
Systems and methods integrating machine learning to optimize the placement of heat sources and thermal sensors through new design clustering methods and to identify potential problems at the early stages of design are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) includes instructions to receive a component location file that indicates the location of a plurality of components on a printed circuit board (PCB), identify an optimal location of a plurality of heat source clusters on a computing device using the component location file, and display the heat source clusters along with their location relative to the computing device on a user interface for view by the user.