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Tag: Semiconductors

Macro Defects Detection

Wafer Macro Defects Detection and Classification

Typical wafer (VLSI) defects are numerous and their detection is a key task in every semiconductor production line. High-resolution scanners are expensive and the process of checking for any local defect is long. Cheaper Macro defects scanning allows to check every wafer rather than recur to sampling-base defect detection. Moreover, our automated wafer defect detection and classification uses state-of-the-art deep learning techniques, able to provide faster and more accurate classifications free of human errors.

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Robot camera on the board of chips

Machine Vision Robots for Semiconductors

Machine vision algorithms are also used to operate robots in the high-precision semiconductor industry. Robots perform these intelligent tasks supported by machine vision software: several methods are currently used to detect defects and classify them, with important economies in both time and money. Robots in the semiconductor industry too can take advantage of deep learning techniques: their main benefit is the dramatic improvement in the defect classification abilities of the robotic devices.

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Visual Inspection of Semiconductors

Visual Inspection of Semiconductors

Semiconductors mass production needs exceptional levels of precision. Among the many processes needed to insure quality and reliability of chips, we were asked to verify that silicon chip masks imprint the right information on silicon wafers. This is a complex, multi-process computer vision task, to solve which we produced an advanced algorithmic software and vision system, the key advantage of which resides in the capture of critical errors early on in the semiconductor production process.

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Computer chipset track - with visible defects

Wafer defect detection by feature matching

Detection of microscopic defect in wafers and printed circuit boards is a standard procedure in the manufacturing process. The time consuming human inspection has been replaced in nearly all production lines with an automatic in-line camera-based examination, which can be very effective usingcomputer vision and image processing technologies to detect any anomalies. Via algorithms of feature extraction and matching, RSIP Vision is able to track defects leading to dramatically improvements in reliability and usability.

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