Efficient fabrication and high reliability of flexible integrated circuit package substrates (FICS) urgently need effective defect detection methods. Based on the defect categories of FICS images, including yellow circuit backgrounds, film backgrounds with speckles, and black backgrounds, a novel rapid detection algorithm of impurity defects in FICS images is proposed. Three successive procedures are indispensable to realize the rapid detection with high accuracy, including image contrast enhancement based on image gray value analysis, obtaining the standard templates to weaken the texture interferences, and the probability calculation of the background feature of each pixel in the edge region between background and defect. Then, the accurate identification of film/black backgrounds and impurity defects in high-density FICS images is achieved. Furthermore, the comparisons of time–accuracy tradeoff obtained by using existing methods (Lk-means, Ostu, GMEM, SPCNN, MBGCT, FTC) and the proposed algorithm are carried out to experimentally verify the feasibility. The defect detection algorithm will facilitate a solution for the high similarity between the inherent texture features of FICS and external impurity defects at micrometer scale and substantially improve the detection accuracy and efficiency of high-density FICS, which will meet the real-time industrial requirement for the rapid fabrication of high-density FICS.

Taking advantage of their inherent dexterity, robotic arms are competent in completing many tasks efficiently. As a result of the modeling complexity and kinematic uncertainty of robotic arms, model-free control paradigm has been proposed and investigated extensively. However, robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying. In this paper, we first propose a new kind of zeroing neural network (ZNN), i.e., integration-enhanced noise-tolerant ZNN (IENT-ZNN) with integration-enhanced noise-tolerant capability. Then, a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms, which improves the performance of robotic arms with the disturbance of noise, without knowing the structural parameters of the robotic arms. The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis. Finally, simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects’ excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments.

Vision inspection has been widely used in the field of defect measurement as a noncontact, nondestructive measurement technique. Conventional visual defect detection methods rely heavily on standard templates and image features, but the standard templates are either difficult to obtain or do not exist in many application fields, especially in flexible integrated circuit (IC) substrate defect detection at a microscopic level. To solve the above problems, a rapid online oxidation defect detection algorithm for micron-level flexible IC substrate oxidation defects, based on the differential geometric approach, is proposed in this paper. First, the sample fitting model is established by using minimal generalized variance as well as the surface fitting model. Second, the defect area of the substrate is determined by analyzing the similarity and membership, which is coded among the pixels by considering the spatial topological structure of the image. Finally, experiments are performed to show that the algorithm proposed in this paper not only has high precision, but also meets industrial real-time requirements for the rapid production of a flexible substrate. Since the method does not depend on the standard template of the original image, it can achieve vision detection effects at high speed and with high precision.