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【2021 Application Example】 HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

In 2017, the launch of the iPhone X made 3D sensor technology used in Face ID highly popular, which drove the development of VCSEL, a core component in the 3D sensor module. In the detection of defects in incoming packaged VCSEL, the use of AI inference models can solve the industry's issue with low yield and improve reliability to 95%.

VCSEL technology currently can be used in many applications and various end consumer markets, including robots, mobile devices, surveillance, drones, and AR/VR. VCSELs are a good solution in applications that require high-speed modulation capabilities, such as cameras and biometrics.

VCSEL technology has broad applications, including in drones. (Pictured: Zoyi Technology's Agricultural Drone)

▲VCSEL technology has a wide range of  applications, including in drones. (Pictured: Zoyi Technology's Agricultural Drone)

VCSEL technology has a wide range of applications, AI technology assists in defect detection

HRT Technology stated that the packaged VCSEL market is also facing strong price competition from competitors, and needs to further reduce costs and enhance product competitiveness. One of the key problems is the replacement of glass lens with epoxy resin lens. The production of traditional glass lenses has high yield, but the cost is higher than that of epoxy resin lenses. Due to the cutting process of epoxy resin, the side wall of cutting lines can easily have rough edges, causing it to be oversized. The release of stress caused by heat during the mounting process will directly cause the optical lens to break.

HRT Technology pointed out that the incoming inspection of VCSEL epoxy resin lenses is very important. Under the constraints of packaging space, the space for fitting the package and optical lens is limited. Moreover, the optical lenses will be confined to a metal frame. If the dimensional tolerances are properly controlled, stress release due to heat during mounting can easily cause the optical lens to break, resulting in a yield loss of up to 10% in the VCSEL package reliability verification, resulting in an increase in production costs.

In order to solve the problems above, HRT Technology hopes to use AI to monitor the size and appearance defects of epoxy resin components in the VCSEL epoxy resin lens incoming stage, verifying whether their dimensions meet specifications, whether the cutting edges are smooth, and whether there are any defects in their appearance. Since traditional incoming material inspection requires a rough visual inspection by humans to distinguish the quality. The problem of image collection needs to be solved first to successfully collect image data.

Therefore, HRT Technology first developed an Automated Optical Inspection (AOI) device, which includes X, Y, Z three-axis motion, high-resolution cameras, and related control software to automatically record images. After collecting the image data, opencv aligns the test image and a normal image to determine differences between the two images, and then pixel mapping is used to compare the pixel area to complete initial screening. Manual labeling is carried out according to the image classification above, including: samples that are normal, have defects in appearance, or have different shape characteristics, and then algorithm training and verification is carried out. Residual neural network (ResNet) or other related algorithms are used for deep learning to identify the quality of lenses.

Implementation of AOI inspection improves production efficiency by 20% and above

Comparing the differences before and after the implementation of AI image inspection, the incoming VCSEL lens inspection before implementation only involved manual inspection of the appearance. The lens is packaged on the VCSEL package that has completed die bonding. After passing the general light up test, the final reliability test (high temperature reflow) is performed. Failed samples go into the rework process.

However, after the implementation of AOI inspection, it can screen defective lenses sooner and reduce the cost of subsequent materials input, it can also reduce the need for rework due to failure, improving yield to 95% and above in the reliability verification. This is expected to help companies reduce production costs by 10% and increase production efficiency by 20% and above.

The difference before and after the introduction of AI image inspection.

▲ The difference before and after implementing AI image detection

HRT Technology pointed out that this technology is an AI application developed based on tiny images. It uses deep learning algorithms to identify defects in the images. The trained network automatically classifies image data to predetermined categories. Defect categories can be determined through reference images, so cumbersome programming is not required.

In the industrial machine vision environment, deep learning is mainly used for classification tasks in applications, such as inspection of industrial products or identification of parts. In the future, with the development of IoT wearable devices and the trend of energy saving, the size of optoelectronic components will continue to shrink. This technology can be applied to the detection of defects in the appearance of other tiny optoelectronic components in the future.

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Realizing the dream of unmanned stores, Magpie Life is building the future of the smartphone industry

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optimize the operational value chain and improve efficiency, and strengthen the core competitiveness of enterprises 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】化身大型AIOT科技遊樂場 海科館華麗轉身好吸睛
Transforming into a Large-Scale AIoT Technology Playground: The Spectacular Makeover of the National Museum of Marine Science & Technology

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【導入案例】維繫遊艇王國美譽 嘉信遊艇導入國內第一套FRP複材超音波智慧檢測
Maintaining the reputation of the “Kingdom of Yachts” - Kha Shing Enterprise introduces the first domestic FRP ultrasonic smart inspection of composite materials

The Kaohsiung-based Kha Shing Enterprise Co, Ltd was established over 40 years ago, and is Taiwan's largest customized yacht company with customers all over America, Europe, Asia, and Australia, earning Taiwan the reputation of the "Kingdom of Yachts" Current FRP hull inspection still relies on traditional methods, such as visual inspection and knocking sounds, which is time-consuming and labor-intensive Kha Shing has applied PAUT array ultrasonic inspection to hull FRP composite materials for the first time, and combined it with AI to interpret ultrasound images, develop complete intelligent solutions, and create emerging markets for inspection companies Kha Shing Enterprise Co, Ltd was formerly Kha Shing Wood Industry Co, Ltd, and was a factory specializing in wood import in Kaohsiung Linhai Industrial Park when it was first established It began to design, manufacture, and sell yachts in 1977 After the second-generation successor of the company, President Kung Chun-Hao entered the 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constructed in sections to facilitate inspection For large yachts over 24 meters long, construction in sections is very time-consuming and labor-intensive To shorten the time of the yacht manufacturing process, Kha Shing Enterprise will first carry out the gel coating process for the hull, and then perform the hand lay-on process The hull manufacturing process has two types of composite material test specimen structures In terms of 54-foot yacht hulls, the hull contains gel coat, core material, fiber and resin, and the total thickness is about 32cmplusmn01cm, which is twice the total thickness of FRP hull without core material of about 16cmplusmn01cm Defects such as incomplete impregnation of glass fiber or residual air bubbles between glass fiber and resin occasionally occur during the manufacturing process The types of defects include insufficient resin, voids, and delamination Once defects occur, the supply of hull materials will be insufficient and yacht delivery will be delayed Schematic diagram of types of FRP hull In order to solve this problem, Kha Shing Enterprise has engaged in technical cooperated with the metal materials industry and the AI technology industry, combining the ultrasonic inspection expertise of the metal materials industry with AI technologies developed by the AI technology industry in recent years to help solve issues of Kha Shing Enterprise with defect detection The method uses PAUT on the composite material structure of yachts, conducts FRP ultrasonic evaluation to determine the thickness of the yacht hull and material properties, and evaluates the ultrasonic probe frequency applicable to the hull structure based on professional ultrasonic experience After testing, a frequency of 5MHz and a probe width of 45mm can successfully find the location and size of defects in the simulated defect test specimen The three parties jointly found defect detection solutions from array ultrasonic evaluation, AI technology model development, and 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engineers 3 High-efficiency process inspection provides defect repair recommendations, reduces damage rate, and improves the strength and quality of composite materials The application of AI technology can optimize the yacht manufacturing process, reduce manual inspection, create added value through the application of AI in Taiwanrsquos yacht industry, increase international purchase orders, and allow Taiwan yachts to continue to enjoy a good reputation in the world Furthermore, this business model has also spread to fields of application related to composite materials, increasing cross-sector market usage It is estimated to contribute approximately NT14 to NT2 billion in economic benefits to Taiwan's equipment maintenance and non-destructive testing market