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AI Achieves 98% Accuracy in 6-Side MLCC Inspection… HanWool Semiconductor Up 14% [Featured Stock]

Kwon Oh Seok
2026-09-01 09:06:18
[Edaily Reporter Kwon Oh Seok ] WINTEC Co.,Ltd.(320000) The stock price is surging in early trading. Buying interest appears to be surging following news that the company’s visual inspection equipment, which utilizes its proprietary artificial intelligence (AI) inspection platform, has yielded positive results.
(Photo courtesy of HanWool Semiconductor)

According to MP Doctor on the 1st, HanWool Semiconductor is trading at 10,130 won as of 9:01 a.m., up 14.46% from the previous trading day.
Earlier today, HanWool Semiconductor issued a press release stating, “Our six-sided visual inspection equipment for multilayer ceramic capacitors (MLCCs) achieved an inspection speed of 13,000 units per minute based on the 0603 standard and a defect detection accuracy of 98%.” The company is currently conducting actual measurement evaluations through an external specialized agency.
This performance test was conducted by capturing images of all six sides of ultra-small MLCCs moving at high speed, with AI determining defect status in real time. According to the company’s internal test results, defect detection accuracy was increased from approximately 95% to approximately 98% while maintaining the inspection speed.
The 0603-sized MLCC is an ultra-small component measuring approximately 0.6 millimeters (mm) × 0.3 mm. During the inspection process, images must be captured from multiple angles while the product is being rapidly transported, and defects such as micro-cracks, chipping, foreign matter, and electrode abnormalities must be identified. In high-speed inspection environments, in particular, the equipment’s performance hinges on technology that reduces both missed and false detections while maintaining throughput.
This equipment incorporates “HawAIe,” an AI inspection platform currently under development by HanWool Semiconductor, and the AI analysis engine “AlohaNet.” HawAIe is a hybrid inspection architecture that combines traditional rule-based image inspection with AI deep learning analysis. Defects with consistent locations and shapes are identified using conventional image processing methods, while areas that are difficult to assess—such as micro-cracks or irregular surface defects—are analyzed by AI.
The company is currently conducting further development with the goal of increasing the AI visual inspection performance—which currently stands at 13,000 units per minute—to 15,000 units per minute by the end of the year. The scope of HawAIe’s application is also being gradually expanded to include the company’s own MLCC post-process inspection equipment, such as composite measuring instruments, indentation testers, and ultrasonic non-destructive testing equipment.

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