Detection of plated through hole defects in printed circuit board with X-ray

Huang, Xingjia; Zhu, Shengcong; Huang, Xuanyu; Su, Bing Hua; Ou, Changping; Zhou, Wenzhong

doi:10.1109/icept.2015.7236817

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…Machine learning combined with image analysis have proven invaluable for quality control and hardware assurance in the PCB manufacturing industry, enabling automated defect detection and visual inspection to a certain degree [69,70,71,72,73,74,75,76,77,78]. Several studies have applied image subtraction to compare a golden, reference image of a PCB design or schematic to a manufactured PCB, whose quality needs to be tested [69,70,71,73,74].…”

Section: B Challenges Associated With Pcbmentioning

confidence: 99%

Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives

Botero,

Wilson,

et al. 2020

Preprint

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In the context of hardware trust and assurance, reverse engineering has been often considered as an illegal action. Generally speaking, reverse engineering aims to retrieve information from a product, i.e., integrated circuits (ICs) and printed circuit boards (PCBs) in hardware security-related scenarios, in the hope of understanding the functionality of the device and determining its constituent components. Hence, it can raise serious issues concerning Intellectual Property (IP) infringement, the (in)effectiveness of security-related measures, and even new opportunities for injecting hardware Trojans. Ironically, reverse engineering can enable IP owners to verify and validate the design. Nevertheless, this cannot be achieved without overcoming numerous obstacles that limit successful outcomes of the reverse engineering process. This paper surveys these challenges from two complementary perspectives: image processing and machine learning. These two fields of study form a firm basis for the enhancement of efficiency and accuracy of reverse engineering processes for both PCBs and ICs. In summary, therefore, this paper presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives. 1

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Section: B Challenges Associated With Pcbmentioning

confidence: 99%

Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives

Botero,

Wilson,

et al. 2020

Preprint

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“…Such failures could make the electronic device dysfunctional, thus those PCBs after SMT process need to be examined, to guarantee that those electronic components are correctly installed on the PCB. A number of approaches are proposed for examining the PCB, including X-ray based approach [2], infrared thermal signatures based approach [3], correlation of inline data based approach [4] and automatic optical inspection (AOI) based approach [5]. Among these methods, AOI has been proved effective and efficient thus widely used in practical factories.…”

Section: Introductionmentioning

confidence: 99%

Improving accuracy of automatic optical inspection with machine learning

Tong

Tian

et al. 2021

Front. Comput. Sci.

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Electronic devices require the printed circuit board (PCB) to support the whole structure, but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices (SMDs) resistors. The automated optical inspection (AOI) machine, widely used in industrial production, can take the image of PCBs and examine the welding issue. However, the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs. This paper proposes a machine learning based method to improve the accuracy of AOI.In particular, we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image. We present a practical scheme including two machine learning procedures to mitigate AOI errors. We conduct experiments with the real dataset from a production line for three months, the experimental results show that our method can reduce the rate of misjudgment from 0.3%-0.5% to 0.02%-0.03%, which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.

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