Nanjing University of Aeronautics and Astronautics

Miracle Automation Engineering Corp. Ltd.

Xiaoming Qian

Peihuang Lou

Chao Meng

Xinhao Wang

Xing Wu

Hangying Zhang

Dahong Guo

Kai Meng

Jiong Zhang

Junquan Zhou

The embodiments of the present disclosure provide a method for an image simulation generation based on a near-field simulation of a computational electromagnetic field, the method comprising: constructing a simulated three-dimensional model based on model parameters; constructing a simulated Kohler illumination model based on a light source parameter; using a degree of similarity change in a synthesized image under incremental aperture diaphragm sampling points as a criterion for approximate convergence of the simulation to determine a count of samples to be used for a balancing combination of simulation cost and accuracy; an optical simulation is performed based on the simulated three-dimensional model and the simulated Kohler illumination model, and a far-field electromagnetic field distribution data is obtained to obtain a simulated image by synthesizing the image. In the generation of a large count of simulated images on the basis of pseudo-random defect dataset generation may be further realized, in the acquisition of a large count of datasets, the dataset of defect inspection model may be trained, in order to achieve a direct detection for patterned wafer defective images and solve the problem of difficult access to reference images in a process of patterned wafer defect detection.

Methods for image simulation, pseudo-random defect dataset generation, and micro and nano defects detection

Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

Malfunction early-warning method for production logistics delivery equipment

A two-dimensional three-degree-of-freedom micro-motion platform structure for high-precision positioning and measurement. Two series flexible hinges are connected in parallel to form a moving pair. One end of the moving pair is fixed, and driving force is applied to the other end. The driving force is amplified by means of a lever to drive the moving pair to translate and rotate. The moving pair drives a first flexible hinge of a platform to rotate, thereby driving the platform to produce corresponding displacement. Four identical moving pairs and four identical first flexible hinges respectively constitute four branch hinges which are different in arrangement; two branch hinges opposite to the platform are in central symmetry and constitute one group, and the four branch hinges are divided into two groups in total. The piezoelectric driving mode can be adopted, and thus the linear displacement in the x-axis direction, the linear displacement in the y-axis direction and the angular displacement in the direction around the z-axis are generated for optical precision positioning and measurement.

Two-dimensional three-degree-of-freedom micro-motion platform structure for high-precision positioning and measurement

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Jiangsu, China

Xiaoming Qian