Papers by Keyword: Defect Type

Abstract: Nowadays, many domestic printing enterprises still rely on artificial visual means to weed out waste, labor-intensive, time-consuming, subjective factors serious influence, the test results not being uniform, especially with the improvement of printing speed, gradually unable to meet the needs of the production. In order to solve the problems in the conventional assay and promote the development of China's printing industry productivity, there is an urgent need to develop the quality of printing online full screen detection system, with the rapid development of modern printing technology, the objective is to test and evaluate. In this paper, machine vision inspection technology, digital image processing technology is used in order to achieve the online testing and feedback of print quality. Dynamic partitioning method is based on standard proofs and proofs of defective segmentation and matching. According to the type of image resolution and the desired accuracy of detecting the type of design image segmentation method, thereby performing the detection and analysis of the color information. Secondary identification defect classification technology is used to change the defect classification, and then display the corresponding defect types. Compared with the traditional printing quality, the high detection print quality inspection speed and precision requirements have been reached, thereby the overall quality of the printed product is improved.

Abstract: Defect of continue casting slab is closely related with technology and working conditions, one productive condition usually leads to the same type of defect. So it has a great practical significance to evaluate the operative process by effective recognition of defect types. In this paper, a damped oscillating signal is acquired by differential method. The defect type of continues casting slab is preliminary qualitative detected via three eigenvalue, the wave peak value(called WPV below), the lagging time of peak(called LTP below) and the peak value difference between the rising and failing edge(called PVD below) which extracting from the differential signal.

Abstract: This paper presents a template-based vision system to detect and classify the nonuniformaties that appear on the semiconductor wafer surfaces. Design goals include detection of flaws and correlation of defect features based on semiconductor industry expert’s knowledge. The die pattern is generated and kept as the reference beforehand from the experts in the semiconductor industry. The system is capable of identifying the defects on the wafers after die sawing. Each unique defect structure is defined as an object. Objects are grouped into user-defined categories such as chipping, metallization peel off, silicon dust contamination, etc., after die sawing and micro-crack, scratch, ink dot being washed off, bridging, etc., from the wafer. This paper also describes the vision system in terms of its hardware modules, as well as the image processing algorithms utilized to perform the functions.