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【2020 Application Example】 Full Inspection AI Implementation in AOI Sealing Process, Reducing Screening Volume by 50%

Miniaturization of products, client demands full inspection

A listed electronic component manufacturer in Taichung, responding to the 5G era injecting new growth momentum into the quartz component industry, especially under the explosion of 5G opportunities, the importance of quartz components will play a more crucial role than in the past in consumer products.

As frequency components move towards miniaturization and at the same time demand high precision, the manufacturing processes are more susceptible to subtle factors, necessitating manufacturers to manage comprehensive data across all aspects including human, machine, material, method, and environment to quickly identify key defective factors in complex production environments.

Differing perceptions of defects, difficulty in enhancing quality consistency

With the trend of miniaturization and complexity of electronic components, visual inspection on the production line has four main functions including measurement, identification, positioning, and inspection, with inspection being the most challenging part as most electronics manufacturers still rely on traditional manual visual inspection.

Taking the PCB industry, where Automated Optical Inspection (AOI) technology has the highest penetration rate, as an example, a research institution once investigated and found that when two individuals inspect the same PCBA board four times, their mutual agreement rate was less than 28%, and the self-agreement rate was only about 44%. Due to differing perceptions of defects among on-site personnel, even automated machine vision can still lead to inconsistencies in product quality due to system settings or differences among quality control staff.

偲捷科技檢測AI化,降低過篩率20%~30%

With the support of the advisory team, collaboration with Sijie Technology aimed at the defects in the sealing process. Based on CNN (Convolutional Neural Network), the integration of multiple models introduced an AI recognition module to aid in the optimization of subsequent AOI tests, aiming to improve the accuracy of inspection equipment.

It is estimated that after introducing AI visual recognition, the over-screening rate could be effectively reduced to 20~30%. Thus, the industry, needing smarter inspection systems, has started applying AI technology to assist AOI equipment in optimizing subsequent screening tests.

AI-powered AOI Inspection Solution Cross-Model Design Concept

▲AI-powered AOI Inspection Solution Cross-Model Design Concept

Sealing AOI Inspection Trial Results

▲Sealing AOI Inspection Trial Results

Reducing false rejects, cutting manual screening workload by 50%

The project, through a deep learning network architecture, reclassifies defects detected including true and false defects, and further classifies them to reduce the false reject rate of the traditional AOI solution. This is anticipated to further aid manual inspectors in reducing more than 50% of the inspection screening volume, addressing current production line issues of relying heavily on manual re-inspection and low efficiency.

Future goals include integrating robotic arms for automatic loading and unloading, and analyzing defect causes to optimize production process parameters.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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這是一張圖片。 This is a picture.
Testing Seat Contact Components AI Intelligent Flaw Detection

With rapid development in 5G, AIOT, automotive electronics, and other downstream sectors, the entire supply chain is expected to benefit from this consumer market As product demand momentum gradually increases, increasing production efficiency and reducing operational costs become the most important issues In order to meet the needs of customers for various packaging types, Yingwei Technology has been committed to developing highly customized test seats However, a resulting pain point is the inability to mass-produce and fully automate operations with machines some tasks still rely on manual execution In this project, the probe part of the test seat was outsourced in 2021, and under current and future large-scale demands, work hours, costs, supply, and quality are issues Yingwei faces The company achieves a defect detection rate of 9995, which seems high, but with an average inspector able to inspect 10,000 needles per day, there would still be 5 defective needles On a test seat that is only 3 cm wide with approximately 1,000 needles, just one defective needle could potentially lead to faulty testing at the customer end As the current operational mode relies on manual visual inspection, external factors such as fatigue or oversight of personnel, and subjective judgment by inspectors may lead to the outflow of defective products, which necessitates strict quality control of contact components We once sought to utilize optical inspections Rule-based for controlling the quality of appearances, but the metallic material of the contact components leads to light scattering, background noise interference, background scratches, and material issues that could result in misjudgments Therefore, we decided to look for AI technology service providers to solve our detection difficulties Developments of Dedicated AOI Line Scan Equipment To meet the needs for inspecting thousands to tens of thousands of probes within our company's IC test seats, traditional surface imaging and individual needle imaging would be too slow to achieve rapid inspection and labor-saving goals In response, the service provider proposed a trial with an AOI dedicated line scan module solution Utilizing a width of 63mm on the X-axis for reciprocal scanning of all probes on the test seat, the tests allowed for the simultaneous scanning of 8-9 probes, significantly enhancing the future detection efficiency of AOI machines This project will proceed with the aforementioned innovative Proof of Concept POC, focusing on the development of the line scanning equipment and performing imaging, learning, and training on both normal and abnormal probes provided by our company, with initial AI model training aimed at preliminary approval This project's customized line-scan imaging module Ideal future imaging result illustration A Single AI Technology Solution for MeasurementDetection Needs Unified use of AI DL CNN learning methods, instead of the current Rule-based system which necessitates defining each defect individually, to meet the needs for abrasion measurement and appearance defect detection of malfunctionsforeign objects When the same machine uses both measurement and detection technologies, not only does it increase costs, but it also affects the detection speed Hence, the service provider recommends the use of a line scan device for imaging Its resolution is sufficient for AI to simultaneously determine appearance defects and assess the condition of needle tip abrasion, as detailed below Line scan pixel imaging displaying needle tip abrasion conditions This AI detection technology meets both measurement and inspection needs for Yingwei, not only bringing more benefits to future probe testing but also introducing an innovative axis in AI technology Change the method of human inspection, enhance work efficiency and product quality After combining both hardware line scan and software AI model training approaches, we successfully ventured into new AOI detection applications Following the AI implementation POC, including the development and validation of a customized line scan module and an initial AI model, the plan is to officially develop the AOI machine next year and integrate it into the IC test seat production line Future Prospects Probe manufacturers upstream and downstream IC factory users both have needs for the AOI inspection machine upstream can ensure probe quality before leaving the factory, while downstream users can use this machine to regularly inspect the condition of numerous IC test seats in hand Given the future demands, the AOI machine is poised to have a significant positive impact on the IC testing industry in the foreseeable future 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-12」

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