Time to Failure Prediction on a Printed Circuit Board Surface Under Humidity Using Probabilistic Analysis

Bahrebar, Sajjad; Ambat, Rajan

doi:10.1007/s11664-022-09668-7

Cited by 6 publications

(3 citation statements)

References 61 publications

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“…We use all not-failed data samples for regression analysis to predict LC values which is around 71.5% of all data means 521 conditions. We only use not-failed data in our regression task, as LC values were obtained from the initial part of the current measurement in the stable part before the failure [90].…”

Section: Algorithms Setupmentioning

confidence: 99%

Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

2022

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Section: Algorithms Setupmentioning

confidence: 99%

Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

2022

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“…Many of these failures arise due to transient water film formation on the surfaces of PCBs or other assembly components exposed to varying climatic conditions (Ambat and Conseil-Gudla 2016). This triggers an electrochemical process between the biased points on the PCBA surface, resulting in a drop in surface insulation resistance and, eventually, an electric short circuit due to electrochemical migration (ECM) (Bahrebar and Ambat 2021), (Bahrebar and Ambat 2022). However, other corrosion failure modes are also possible depending on the materials and environmental conditions (Bahrebar et al 2018), (Rastayesh et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Using Predictive Analytics Approaches to Investigate Climatic Reliability and Humidity Robustness issues of PCBA under Different Conditions

Bahrebar,

Ambat

2023

Proceeding of the 33rd European Safety and Reliability Conference

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Nowadays, climatic reliability and humidity robustness of electronic devices has become significant issue for both consumer and industrial electronics due to various reasons. One reason for this increased problem is the widespread use of electronics in many locations. The climatic reliability of electronics is attributed to the interaction of external climatic conditions and printed circuit board assembly (PCBA) characteristics as the main part of each electronic device, which compromise the performance of electronics due to the electrochemical failure process. In order to improve the reliability of electronics, requires a detailed understanding of the synergetic and interaction effects of various controllable factors, such as humidity, temperature, pitch distance, voltage, contamination types, and contamination levels. Moreover, it is crucial for reliability assessment to understand the relative importance of factors and their levels to take remedial action at an earlier stage based on selecting the best PCBA material, soldering process, and optimizing the design in desired tasks for particular applications and climatic conditions. This study presents the most suitable approach and prediction model based on the input datasets by using a combination of statistical analysis, probabilistic approaches, and machine learning algorithms to predict leakage current (LC), time to failure (TTF), failure state, and highly risky conditions, that could provide a better perspective of PCBA reliability and helps to reduce electronic waste due to failure.

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“…Certain amounts of residues remaining on a PCBA surface due to its hygroscopic nature attract humidity from the surrounding environment and build up water layer, act as corrosion accelerated factors, and contribute to deterioration in the electronic device's functionality [4], [5]. The soldering process, especially wave soldering because of the use of liquid flux by spraying process, introduces ionic residues on the PCBA surface during the manufacturing process, which along with other factors, affects the corrosion failures related to humidity on the PCB surface [6], [7]. Most of the no-clean flux systems today are based on weak organic acid (WOA) activators [8].…”

Section: Introductionmentioning

confidence: 99%

Probability of Failure due to Electrochemical Migration on Surface Mount Capacitor under Electrolyte Condition with same Contamination and Conductivity Level

Bahrebar

Ambat

2023

2023 IMAPS Nordic Conference on Microelectronics Packaging (NordPac)

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The effect of three common no-clean solder flux residues (adipic acid, glutaric acid, and succinic acid) as contamination types at three low levels (same concentrations) as well as in the same conductivity value in comparison with sodium chloride (NaCl) on time to failure (TTF) due to electrochemical migration (ECM) on the surface mount capacitors (SMCs) were investigated. The relation between TTF and leakage current (LC), conductivity, number of failures, as well as the probability of failure (PoF) using applicable probabilistic distributions in various conditions combined with diverse contamination types/levels and voltage based on many replications were studied.

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Time to Failure Prediction on a Printed Circuit Board Surface Under Humidity Using Probabilistic Analysis

Cited by 6 publications

References 61 publications

Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

Using Predictive Analytics Approaches to Investigate Climatic Reliability and Humidity Robustness issues of PCBA under Different Conditions

Probability of Failure due to Electrochemical Migration on Surface Mount Capacitor under Electrolyte Condition with same Contamination and Conductivity Level

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