Assessing the Effectiveness of Different Test Approaches for Power Devices in a PCB

Piumatti, D.; Borlo, Stefano; Reorda, M. Sonza; Bojoi, Iustin Radu

doi:10.1109/jestpe.2020.3013229

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…The catastrophic faults are modeled by adding different electrical switches in the circuit diagram of the SSUT. In particular, the serial switches, the parallel switches, and the topological switches are added with some rules now discussed [37]. The fault list is generated by introducing: The input stimuli are electrical quantity, as voltage, applied to the printed circuit board (PCB) input ports.…”

Section: Proposed Approachmentioning

confidence: 99%

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Multilevel Simulation Methodology for FMECA Study Applied to a Complex Cyber-Physical System

et al. 2020

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Complex systems are composed of numerous interconnected subsystems, each designed to perform specific functions. The different subsystems use many technological items that work together, as for the case of cyber-physical systems. Typically, a cyber-physical system is composed of different mechanical actuators driven by electrical power devices and monitored by sensors. Several approaches are available for designing and validating complex systems, and among them, behavioral-level modeling is becoming one of the most popular. When such cyber-physical systems are employed in mission- or safety-critical applications, it is mandatory to understand the impacts of faults on them and how failures in subsystems can propagate through the overall system. In this paper, we propose a methodology for supporting the failure mode, effects, and criticality analysis (FMECA) aimed at identifying the critical faults and assessing their effects on the overall system. The end goal is to analyze how a fault affecting a single subsystem possibly propagates through the whole cyber-physical system, considering also the embedded software and the mechanical elements. In particular, our approach allows the analysis of the propagation through the whole system (working at high level) of a fault injected at low level. This paper provides a solution to automate the FMECA process (until now mainly performed manually) for complex cyber-physical systems. It improves the failure classification effectiveness: considering our test case, it reduced the number of critical faults from 10 to 6. The remaining four faults are mitigated by the cyber-physical system architecture. The proposed approach has been tested on a real cyber-physical system in charge of driving a three-phase motor for industrial compressors, showing its feasibility and effectiveness.

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Section: Proposed Approachmentioning

confidence: 99%

“…A functional stimulus is an input signal that complies with the system design specifications. The saboteur injects a single catastrophic fault [37] into the SSUT structural model at the start of a simulation, as discussed in [3]. 8.…”

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confidence: 99%

Multilevel Simulation Methodology for FMECA Study Applied to a Complex Cyber-Physical System

et al. 2020

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“…The extensive utilization of automatic test equipment, including analog signature analysis (ASA) testing, in-circuit testing, and functional testing, has played a key role in ensuring the quality and reliability of circuit boards [1,2]. The ASA technique applies a sine-wave voltage to a selected pin, records current values on a voltage-current (VI) characteristic curve, and detects faults by comparing the VI curves of healthy and faulty circuit boards.…”

Section: Introductionmentioning

confidence: 99%

Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve

Pan,

Zhao,

Zhao

et al. 2024

Electronics Letters

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The accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy of fault detection using the VI curve. First, a loss function with multiple objectives is formulated by integrating curve fitting and prediction interval. The aim is to achieve the curve fitting between current and voltage while simultaneously determining the optimal upper and lower bounds of the prediction interval. Second, an ensemble approach is employed to reduce model uncertainty and derive the ultimate current predictions and intervals. These predictions and intervals are then used in a comparative approach to automatically detect faults in circuit boards. Finally, the effectiveness of the proposed algorithm in improving the accuracy of fault detection is verified on experimental circuit boards.

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A novel motherboard test item yield prediction model based on parallel feature extraction

Yan,

Zhai,

2024

J. Phys.: Conf. Ser.

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Functional testing of motherboards in Surface Mount Technology (SMT) assembly lines is crucial. Accurate yield prediction for each test item optimizes testing strategies, reduces costs, and ensures test coverage. Manual estimation of test item yields remains common, hindering accurate on-site predictions. Existing research on motherboard yield lacks predictions for individual test items and ignores temporal correlations during placement. This paper introduces a method, a convolutional bidirectional long short-term memory attention network (CBA-Net), which combines a convolutional neural network and a bidirectional long short-term memory network with an attention mechanism for parallel processing. It preprocesses historical test data, leveraging both networks to identify key features and extract temporal correlations. The attention mechanism optimizes yield predictions by assigning weights to information at different time steps. Experimental validation using actual production data demonstrates that the proposed method performs better compared to traditional models.

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Assessing the Effectiveness of Different Test Approaches for Power Devices in a PCB

Cited by 4 publications

References 29 publications

Multilevel Simulation Methodology for FMECA Study Applied to a Complex Cyber-Physical System

Multilevel Simulation Methodology for FMECA Study Applied to a Complex Cyber-Physical System

Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve

A novel motherboard test item yield prediction model based on parallel feature extraction

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