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Abstract Cyclic degradation in flexible electronic inks remains a key challenge while their deployment in life critical applications is ongoing. The origin of electrical degradation of a screen-printed stretchable conductive ink with silver flakes embedded in a polyurethane binder is investigated under uniaxial monotonic and cyclic stretching, using in-situ confocal microscopy and scanning electron microscopy experiments, for varying ink thickness (1, 2, and 3 layers, each layer around 8–10 μ m) and trace width (0.5, 1, and 2 mm). Cracks form under monotonic stretching, and the evolution of crack pattern (density, length and width) with applied strain is affected by ink thickness such that the 3-layer ink exhibits larger normalized resistance but slightly lower resistance than the 1-layer ink up to strains of 125%. For cyclic stretching, the crack density and length do not evolve with cycling. However, the cracks widen and deepen, leading to an increase in resistance with cycling. There exists a strong correlation between fatigue life, i.e. the number of cycles until a normalized resistance of 100 is reached, and the strain amplitude. The normalized resistance increase rate with respect to cycling is also found to scale with strain amplitude. The rate of change in resistance with cycling decreases with ink thickness and trace width. For practical applications, thicker ( ⩾ 25 μ m) and wider (⩾2 mm) inks should be used to lower resistance increases with repeated deformation.
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This content will become publicly available on April 28, 2026
Reliable Board-Level Degradation Prediction with Monotonic Segmented Regression under Noisy Measurement
The increasing complexity of electronic systems in autonomous electric vehicles necessitates robust methods for forecasting the degradation of critical components such as printed circuit boards (PCBs). Various time series forecasting methods have been investigated to predict in-situ resistance degradation under vibration loads. However, these methods failed to capture the degradation trend under strong measurement noise. This paper introduces Monotonic Segmented Linear Regression (MSLR), a novel approach designed to capture monotonic degradation trends in time series data under significant measurement noise. By incorporating monotonic constraints, MSLR effectively models the non-decreasing behavior characteristic of degradation processes. To further enhance reliability of the prediction, we integrate Adaptive Conformal Inference (ACI) with MSLR, enabling the estimation of statistically valid upper bounds for resistance degradation with high confidence. Extensive experiments demonstrate that MSLR outperforms state-of-the-art time series forecasting baselines on real-world PCB degradation datasets.
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- Award ID(s):
- 1956313
- PAR ID:
- 10593475
- Publisher / Repository:
- 2025 IEEE VLSI Test Symposium
- Date Published:
- ISBN:
- 979-8-3315-2144-8
- Format(s):
- Medium: X
- Location:
- Tempe, AZ, USA
- Sponsoring Org:
- National Science Foundation
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