PCB Digital Twin Failure Forecasting Under Environmental Stress
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Solution Overview
Problem
The initial design and prototyping of printed circuit boards (PCBs) are costly and time-consuming, and it is difficult to ascertain the reliability of PCB designs under different operating conditions, especially as they can be affected by various environmental stressors leading to device failures.
Innovation Solution
A method and system that integrate sensors on PCBs to generate and transmit data, using physics-based reliability models and machine learning techniques to predict real-time failure rates, incorporating historical failure data and environmental stressors such as temperature, vibrations, and humidity to forecast potential failures and performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional PCB design and prototyping methods are used, then PCB functionality can be achieved, 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 operational behavior. This digital replica allows reliability prediction and testing without requiring physical prototypes, thereby reducing both time and cost while maintaining prediction accuracy.
Solution Approach 2:
The digital twin model incorporates material properties, design parameters, and environmental stressors beforehand to predict PCB reliability before physical testing or deployment. This preliminary virtual assessment eliminates the need for iterative physical prototyping.
2Reliability
If physical prototyping and testing are performed to ascertain PCB reliability, then reliability data can be obtained, but the process becomes costly and arduous
Solution Approach 1:
The patent replaces physical mechanical testing and prototyping with computational simulations and data analytics within the digital twin framework. Environmental stressors and failure modes are simulated virtually rather than through physical experimentation, making reliability assessment easier and more accessible.
3Adaptability or versatility
If PCBs are tested under various environmental stressors, then reliability under different conditions can be ascertained, but the complexity and cost increase
Solution Approach 1:
The digital twin model serves multiple functions: it predicts reliability under various environmental conditions, identifies failure modes, optimizes design parameters, and validates design modifications all within a single unified platform. This eliminates the need for separate testing systems for each condition.
Data Source
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 sensor data, transmitting the sensor data, and receiving 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 a real-time failure prediction signal based on the physics-based reliability models, and reporting the real-time failure prediction signal. The circuit board may include printed circuit boards (PCBs), particularly additively-manufactured printed circuit boards (AM-PCBs).