PCB Digital Twin Reliability Prediction Under Environmental Stress
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Solution Overview
Problem
The initial design and prototyping of circuit boards (PCBs) is costly and time-consuming, and assessing their reliability under different operating conditions is difficult, especially due to environmental stressors, leading to device failures.
Innovation Solution
A method and system that integrate sensors on PCBs to collect data, generate physics-based reliability predictions using empirical models and machine learning, and utilize a digital twin to forecast real-time failure probabilities, enabling proactive maintenance and design improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional PCB design and prototyping methods are used, then design flexibility is maintained, but the process becomes costly and time-consuming
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the PCB that replicates its physical characteristics, materials, and structure. This digital replica enables virtual prototyping, testing, and validation without requiring physical prototypes, thereby reducing both time and cost while maintaining design flexibility. The digital twin can be iterated upon rapidly in the virtual domain before committing to physical manufacturing.
2Reliability
If physical prototyping is performed to assess reliability, then accurate reliability data is obtained, but the process becomes time-consuming and costly
Solution Approach 1:
The patent performs reliability assessments in the digital domain before physical prototyping or deployment. The digital twin is subjected to virtual environmental stressors (thermal cycling, humidity, vibration, etc.) and operational conditions to predict reliability outcomes in advance. This preliminary virtual testing provides accurate reliability data without requiring time-consuming physical tests, enabling design modifications before manufacturing commitments.
3Reliability
If PCBs are tested under environmental stressors, then reliability under real conditions is assessed, but device failures occur during testing
Solution Approach 1:
The patent applies environmental stressors to the digital twin rather than the physical PCB. The virtual model experiences simulated thermal cycling, humidity exposure, vibration, and other environmental conditions that would normally risk damaging physical devices. Since the digital twin is a virtual replica, it can be subjected to extreme or prolonged stress conditions without risk of actual damage, allowing comprehensive reliability assessment while preserving the physical asset.
4Reliability
If design modifications are made to improve reliability, then PCB performance under stress improves, but the design process becomes more complex
Solution Approach 1:
The patent implements an iterative feedback loop where the digital twin is subjected to virtual environmental testing, reliability performance is measured and analyzed, and results are fed back to guide design modifications. This systematic feedback process identifies specific weak points and prioritizes modifications based on their impact on reliability. The feedback mechanism manages design complexity by focusing modifications on high-impact areas rather than requiring comprehensive redesign, while continuously improving stress resistance through data-driven iterations.
Data Source
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AI summary
A system and method for performing time-dependent reliability prediction of a printed circuit board (PCB) embedded in a sensor that monitors the health (viz., performance) of operating equipment subject to different environmental stressors. The method includes developing a digital twin (DT) of the physical PCB, generating (420) sensor data, transmitting (430) the sensor data, and receiving (440) sensor data and historical conditional data by the twinning module, wherein the historical condition data includes known failure data of one or more electronic components of the circuit board based on an internal condition or and external condition. The method further includes embedded physics-based reliability models informed by inputs from the sensor data and the historical conditional data, generating (460) a real-time failure prediction signal based on the physics-based reliability models, and reporting (470) the real-time failure prediction signal. The circuit board may include printed circuit boards (PCBs), particularly additively-manufactured printed circuit boards (AM-PCBs).