The Increasing Importance of Utilizing Non-intrusive Board Test Technologies for Printed Circuit Board Defect Coverage

Johnson, Michael

doi:10.1109/autest.2018.8532499

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Regardless of the testing approach, solutions are expected to be flexible and non-invasive (Johnson, 2018). The use of artificial neural networks in intelligent test systems has been explored in several works.…”

Section: Related Workmentioning

confidence: 99%

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Oliveira

Bastos-Filho

Oliveira

2022

Preprint

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Quality control is a critical aspect of modern electronic circuit industry. In addition to being a prerequisite for proper functioning, circuit quality is closely related to safety, security, and economics. Deep learning models have been extensively used in embedded systems testing and anomaly detection. However, performance is heavily dependent on the data available for training. Often, the number of samples or even its quality is limited. This leads to poor training process and low performance. We present a solution to improve anomaly detection in embedded systems by transforming time signals acquired from the printed circuit board under test. The proposed approach is experimentally validated in two autoencoder-based anomaly detection systems. Hence, two types of signals are analyzed: electric current and thermographic signatures. In both cases, electrical or thermographic signals are pre-processed prior to being converted into spectrogram images, which are then used to train and test the autoencoder. The achieved anomaly detection accuracy improvement for the thermographic case is 71%, compared with the raw data. For the electric current case, we show how data transformations enable autoencoder training where, using raw data, training is not feasible. In this case, we find accuracy improvements of up to 98%. The results indicate that, even in a scenario where the available data are limited, it is possible to achieve an acceptable performance using the proposed technique.

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Section: Related Workmentioning

confidence: 99%

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Oliveira

Bastos-Filho

Oliveira

2022

Preprint

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“…Meanwhile, the concept of industry 4.0 [3] poses an additional challenge for system testing: a high level of product customization [4] [5]. The use of intrusive test methods that require test points on the circuit or flying probe analyzers shows many disadvantages [6], such as high cost and complexity. On the other hand, a flexible and non-intrusive method, capable of detecting defects in both hardware and firmware, shall provide more agility and reliability to the design flow of embedded systems [7].…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive Embedded Systems Anomaly Detection using Thermography and Machine Learning

Oliveira¹,

Bastos-Filho²,

Oliveira³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Quality control in electronic system manufacturing is achieved mainly through system testing. Device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably and processes based on manual inspections have become outdated and inefficient. The concept of Industry 4.0 has enabled the manufacturing of customized products based on customers’ demands, which demands a high degree of flexibility in production processes, with low cost and without placing numerous test points. In this paper, we propose two automated test solutions based on machine learning and thermographic analysis. We propose deploying autoencoders and random forest in two different manners to detect firmware or hardware anomalies based on the circuit board’s temperature signature. We validate our proposal using two firmware versions running independently on the test board. We obtained an anomaly detection rate above 98%. In the random forest approach, we require all data classes for training, whereas the autoencoder only requires the reference class, which is expected in real scenarios.

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“…Physical contacts to the soldering points are necessary to facilitate a test. However, increasing density of the printed circuit board assembles (PCBAs) is diminishing these physical contacts [3], and ICT becomes less and less feasible and functional testing is heavily relied on. Functional testing [4] is an essential buyoff of any products and only allows an overall/global fault detection, leaving behind the information on which components on the products cause the failure.…”

Section: Introductionmentioning

confidence: 99%

A Compact High-Resolution Resonance-Based Capacitive Sensor for Defects Detection on PCBAs

2020

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The Increasing Importance of Utilizing Non-intrusive Board Test Technologies for Printed Circuit Board Defect Coverage

Cited by 8 publications

References 2 publications

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Non-intrusive Embedded Systems Anomaly Detection using Thermography and Machine Learning

A Compact High-Resolution Resonance-Based Capacitive Sensor for Defects Detection on PCBAs

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