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Wafer map defect patterns classification based on a lightweight network and data augmentation

CAAI Transactions on intelligence Technology 2023年第3期8卷 1029-1042页

作者： Naigong Yu Huaisheng Chen Qiao Xu Mohammad Mehedi Hasan Ouattara Sie Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System BeijingChina Engineering Research Center of Digital Community Ministry of EducationBeijingChina College of Robotic UniversitéFélix Houphouët‐BoignyAbidjanCôte d'Ivoire

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production *** the wafer testing stage,deep learning methods are widely used in wafer defect detection due to their powerful feature extraction ***,most of the current wafer defect patterns classification models have high complexity and slow detection speed,which are difficult to apply in the actual wafer production *** addition,there is a data imbalance in the wafer dataset that seriously affects the training results of the *** reduce the complexity of the deep model without affecting the wafer feature expression,this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet *** addition,to reduce the impact of data imbalance on model training,this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden *** method proposed in this paper has an average accuracy of 95.4%on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs,and takes only 22.99 s to detect 1000 wafer *** with the original PeleeNet network without optimization,the number of parameters and FLOPs are reduced by 92.68%and 58.85%,*** augmentation on the minority class wafer map improves the average classification accuracy by 1.8%on the WM‐811K *** the same time,the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

关键词： convolutional autoencoder lightweight network wafer defect detection

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DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function

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Journal of beijing Institute of Technology 2024年第4期33卷 287-297页

作者： Ting Hu Siyuan Cheng Chang Liu Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Space Star Technology Co. LtdBeijing 100086China Research Institute of Intelligent Wire-less Communication Network Technology Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China

Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread ***,few works have been payed on the estimation of the two degra-dation *** learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly ***,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread *** the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation *** image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.

关键词： Dirichlet network point spread function spectral response function hyper-spectralimage multi-spectral image

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Diffuse optical monitoring of blood flow and oxygen saturation in deep tissues

Diffuse optical monitoring of blood flow and oxygen saturati...

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Optical Sensors, Sensors 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Li, Zhe Feng, Jinchao Sun, Zhonghua Liu, Pengyu Jia, Kebin Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

A hybrid diffuse optical monitoring device was developed using multi-wavelength lasers for both diffuse correlation spectroscopy (DCS) and diffuse optical spectroscopy (DOS). In-vivo experiments have validated the pro... 详细信息

ISBN: (纸本)9781557528209

关键词： Tissue

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Face super-resolution via discriminative-attributes 1

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Dong, Ning Li, Xiaoguang Li, Jiafeng Zhuo, Li Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

The limited resolution of cameras and the wide field of the video surveillance systems lead to low quality captured facial images and difficult to identify. Face super-resolution methods are proposed to enhance the resolution of facial images. However, it remains a challenging issue to restore discriminative features to identify a specific person in surveillance videos. An algorithm that helps face super-resolution and recognition with the aid of discriminative-attributes is proposed in this paper. We introduce discriminative-attributes for face recognition to recover discriminative features in the reconstructed facial images. Attributes with more discriminative power are selected to input the network together with the low-resolution face image. The experimental results of the LFW-a benchmark test show that our method achieves promising results in both subjective visual quality and face recognition accuracy. © Springer Nature Switzerland AG 2019.

关键词： Face recognition

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An Infrared Target Detection Method Based on Dual Attention Network for Feature Distillation 43

An Infrared Target Detection Method Based on Dual Attention ...

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43rd Chinese Control Conference, CCC 2024

作者： Xu, Yifei Yang, Jinfu Wang, RunShi Li, Haoqing Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9789887581581

Due to the lack of infrared image data, infrared target detection is still a challenging task. To increase the performance of infrared target detection, this paper proposes a feature distillation method based on dual attention meta-network. Specifically, a dual attention meta-network is designed to transfer feature knowledge from visible to infrared images. The network includes two parts: Spatial Attention Meta-network (SAM) and Channel Attention Meta-network (CAM). In SAM, the distance between visible and infrared features is minimized by designing L2 loss, so that the knowledge of visible features contained in the teacher model is transferred to the student model. In CAM, the similarity between visible and infrared features is calculated as the distillation strength of feature connections to achieve more efficient feature distillation. In addition, to avoid the transfer of negative information, a feature selection method based on mutual information evaluation is proposed, in which the feature connections with larger mutual information are retained by setting a mutual information threshold. Experiments on public infrared detection dataset show that the proposed method outperforms the state-of-the-art feature distillation methods. In particular, the proposed method achieves an accuracy of 94.35% in infrared target detection, which is a 5.85% improvement compared to one without feature distillation. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Infrared imaging

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Dual arm coordination with coordination diagram based on teleoperation demonstration

Dual arm coordination with coordination diagram based on tel...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Zuo, Guoyu Xu, Zichen Liu, Lu Gong, Daoxiong Li, Jianfeng Faculty of Information Technology Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665436786

Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual-arm coordination algorithm to improve the efficiency of coordination by considering both robot's actions and operating sequences for tasks that require multiple operations with both arms. Teleoperation demonstration is performed to obtain the robot's human-like motion trajectories, so as to reduce the probability of collisions of the two robotic arms. The coordination diagram in time domain is designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. Simultaneously, a coordination pair generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories in addition to the action coordination. The effectiveness and efficiency of our method are verified on the experiment of the drug sorting task in nursing homes on the ABB YuMi robot model. © 2021 IEEE.

关键词： Remote control

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Self-alignment for Strapdown INS Using Real-time Adaptive Filter

Self-alignment for Strapdown INS Using Real-time Adaptive Fi...

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2016 IEEE Chinese Guidance, Navigation and Control Conference (IEEE CGNCC2016)

作者： ZHAO Jian PEI Fu-jun LIU Hong-yun Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Electronic Information & Control EngineeringBeijing University of Technology IEEE

ISBN: (纸本)9781467383196

As it is difficult to get an accurate mathematical noise model under various dynamic interference conditions,Strap-down Inertial Navigation system(SINS) was difficult to realize *** the fuzzy adaptive Kalman filter can be used to realize the SINS self-alignment,the algorithm complexity is high and the error term is *** solve this problem,a novel self-alignment method using real-time adaptive filter was developed in this *** method is based on the theory of fuzzy adaptive adjustment and used the filter stability as the *** exponential function was used to replace the fuzzy inference calculation,which eliminated the complex process of fuzzy,fuzzy inference and *** stability and accuracy of the adaptive filter was also ***,the simulation process was used to test the algorithm of this method,and the results demonstrated that the self-alignment using real-time adaptive filter method reduced the complexity of the algorithm and had better stability and alignment accuracy.

关键词： filter Kalman Self-alignment for Strapdown INS Using Real-time Adaptive Filter

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Wafer bin map inspection based on DenseNet

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Journal of Central South University 2021年第8期28卷 2436-2450页

作者： YU Nai-gong XU Qiao WANG Hong-lu LIN Jia Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

Wafer bin map(WBM)inspection is a critical approach for evaluating the semiconductor manufacturing *** excellent inspection algorithm can improve the production efficiency and *** paper proposes a WBM defect pattern inspection strategy based on the DenseNet deep learning model,the structure and training loss function are improved according to the characteristics of the *** addition,a constrained mean filtering algorithm is proposed to filter the noise *** model prediction,an entropy-based Monte Carlo dropout algorithm is employed to quantify the uncertainty of the model *** experimental results show that the recognition ability of the improved DenseNet is better than that of traditional algorithms in terms of typical WBM defect *** the model uncertainty can not only effectively reduce the miss or false detection rate but also help to identify new patterns.

关键词： wafer defect inspection convolutional neural network DenseNet model uncertainty

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Attention-Aware Based Differentiable Architecture Search Network for Waste Recognition 13

Attention-Aware Based Differentiable Architecture Search Net...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2023

作者： Zhang, Qiyu Han, Honggui Li, Fangyu Du, Yongping Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9798350315196

Manually designed deep neural networks have successfully forwarded waste recognition tasks in the resource recycling field. However, due to the diversity of waste samples, the feature extraction ability of inherently designed models fails to fully satisfy the requirements of real world applications. In this article, an attention-aware based differentiable architecture search (ADAS) network for waste recognition is proposed, which self-organizes to generate an optimal network structure according to diverse waste data. First, a structured search space with attention-aware modules is designed to enhance the diverse feature representations of waste data. Secondly, an efficient and differentiable structure search is achieved by continuously relaxing the representation of the network architecture and search space. Finally, the optimal architecture search process is evaluated by a bi-level optimization algorithm. Experimental results show that the proposed method achieves more satisfactory classification performances than the manually designed ResNet, DenseNet networks in the TrashNet dataset and the self-built household waste dataset. © 2023 IEEE.

关键词： Network architecture

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Image-Based Visual Servoing under Field of View Constraints for Robot Manipulators 12

Image-Based Visual Servoing under Field of View Constraints ...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Zuo, Guoyu Liu, Yan Huang, Gao Gong, Daoxiong Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The challenge in image-based visual servoing is to deal with the visibility constraints, which require the image features to always remain in the field of view (FOV) of the camera. In this paper, a novel constraint function is proposed to address this problem. The FOV constraint function represented by the coordinates of the features and the region boundaries defined in the image is added to the classical proportional controller, which leads the image features from the margin to the center of the image. Simulation results verify the efficiency of the modified controller by controlling a robot manipulator with eye-in-hand configuration. © 2022 IEEE.

关键词： Robot applications

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