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STROBOSCOPIC DATA-DRIVEN, INTEGRATED, AND INTELLIGENT machine learning-BASED ALGORITHMS FOR SEMICONDUCTOR WAFER INSPECTION 18

STROBOSCOPIC DATA-DRIVEN, INTEGRATED, AND INTELLIGENT MACHIN...

引用

ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Han, Changheon Chun, Heebum Lee, ChaBum Jun, Martin Byung-Guk Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Texas A&M Univ Mike Walker Dept Mech Engn 66 College Stn TX 77843 USA Indiana Mfg Competitiveness Ctr IN MaC W Lafayette IN 47906 USA

ISBN: (纸本)9780791887240

In the manufacturing process, it is important to improve the yield rate, which significantly affects manufacturing costs. Especially, even a very minor flaw can ruin the entire production of semiconductors on a wafer. The semiconductor industry treats an inspection process significantly;however, the inspection process mostly depends on human inspectors. Therefore, it is a time-consuming process and affected by human factors that cause serious challenges during the inspection process. Moreover, skilled laborers are required to ensure the integrity of inspections. To achieve an effective inspection method for reducing cost and improving yield rate, this paper introduces a data-driven, integrated, and intelligent framework that enables an inspection of particle defects on a semiconductor wafer surface and the location of the particles by machine learning-based algorithms. There are two processes in the framework;scanning a semiconductor wafer surface to generate raw image sets and investigating images to predict their status with or without defects. The stroboscopic imaging technique was utilized to in-line scan the wafer while the wafer is rotating on the spindle. Thus, instead of taking static images one by one, sequence of the wafer surface images can be obtained while the wafer is rotating which can reduce time for taking images. Once the surface images of a wafer are collected, in the second stage, machine-learning techniques are utilized to investigate the status of a semiconductor wafer. There are two subdivided steps in this stage;determining whether a semiconductor wafer has a defect and locating the position of a defect (a particle). As images by visible light from a microscope are used which has low contrast, it is not easy to distinguish the features of defects from backgrounds. Hence, image-processing techniques are applied to emphasize defects in images. Moreover, the number of images for training is amplified by augmenting images with varying contrast an

关键词： Stroboscopic Smart Manufacturing Computer Vision machine learning Semiconductor Manufacturing Data-driven Metrology

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Distributed Denial-Of-Service (DDoS) in software-Defined Network Based on Artificial Neural Network and Binary Multi-Neighborhood Artificial Bee Colony (BMNABC) Algorithm 3

Distributed Denial-Of-Service (DDoS) in Software-Defined Net...

引用

3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Ateeyah, Esam Seker, Cihat Karabuk University Electrical and Electronics Engineering Department Karabuk Turkey

ISBN: (纸本)9798350340358

In this paper, an intelligent intrusion detection system in a software-based network with metaheuristic algorithm is presented. In the proposed system, the controllers use the binary multi-neighborhood artificial bee colony Algorithm, to select the features. In the proposed method, the artificial neural network is used to classify the attach and non-attach data. For comparison of the results the machine learning algorithm such as support vector machine, decision tree, Ensemble, Navie Base, and K-NN methods has been used and evaluated. the best results have been obtained for artificial neural network and it was 98.99%, 98.97%, 98.94%, 98.95%, and 97.95% for accuracy of train, accuracy of test, precision test, and recall test, and F1 test, respectively. Also, the worst results have been obtained for the DT method and it was 97.54%, 97.56%, 97.42%, 97.76%, and 97.09% for accuracy of train, accuracy of test, precision test, and recall test, and F1 test respectively. © 2023 IEEE.

关键词： Neural networks

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software Defect Prediction Using Deep Semantic Feature learning

Software Defect Prediction Using Deep Semantic Feature Learn...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Dwarakanath, B. Renuka, S. Brindha, T.V. Ebinezer, M.J.D. Srm Institute of Science and Technology Ramapuram Information Technology College of Engineering and Technology Chennai India Vasavi College of Engineering Information and Technology Hyderabad India Sri Venkateshwara College of Engineering and Technology Computer Science and Engineering Tiruvallur India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350313413

The software defect prediction (SDP) methodology has the potential to increase software reliability by foreseeing any ominous problems in its source code. Nevertheless, as recent research has demonstrated, creating defective prediction models is a challenging issue. Several research methods for predicting source code defects have been proposed over time. However, the majority of previous research has concentrated on traditional feature extraction and modelling. Traditional methods frequently fail to locate the contextual information needed to build reliable prediction deep learning models in source code files. On the other hand, defect prediction semantic feature techniques have only lately changed and advanced. Using such techniques, it would be possible to forecast suspicious problems by automatically extracting contextual information from source code files. We suggest employing deep learning, a potent representation-learning approach, to fill the gap between defect prediction data and semantics. In order to automatically extract semantic properties from word vectors generated by program abstract syntax trees (AST) for file-level defect prediction models and source code updates for change-level defect prediction models, we use a Long-Short Term Memory (LSTM) model. Two tasks - one for defect prediction at the file level (within-project defect prediction and cross-project defect prediction) and one for defect prediction at the change level (within-project defect prediction and cross-project defect prediction) - were used to assess our methodology. Our findings demonstrate that LSTM-AST-based semantic features can greatly enhance fault identification tasks. The findings demonstrate that the deep learning model LSTM-AST is a practical choice for medium to low sparsity ratio datasets and that, in comparison to other machine models, it performs typically better in high and extremely high sparsity ratio datasets. © 2023 IEEE.

关键词： abstract syntax trees (AST) Deep learning (DL) Long-Short Term Memory (LSTM) software defect prediction (SDP) source code

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Improving polygon image segmentation by enhancing U-Net architecture

Improving polygon image segmentation by enhancing U-Net arch...

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2023 international conference on machine learning and Automation, conf-MLA 2023

作者： Li, Da Software College Jiangxi Agricultural University Nanchang330045 China

The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model's capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Medical imaging

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2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023 - Proceeding

2023 International Conference on Artificial Intelligence and...

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2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

ISBN: (纸本)9781665456272

The proceedings contain 82 papers. The topics discussed include: hybrid model for email spam prediction using random forest for feature extraction;social engineering defender (***): human emotion factor based classification and defense against social engineering attacks;a survey paper on precision agriculture based intelligent system for plant leaf disease identification;digital twin of a musculoskeletal system;detecting copy move image forgery using a deep learning model: a review;fabric fault and extra thread detection using convolutional neural network;performance evaluation of classifiers for ECG signal analysis;software requirement classification using machine learning algorithms;a review on diagnosis of lung cancer and lung nodules in histopathological images using deep convolutional neural network;video prediction using recurrent neural network;a sustainable social media smart model for the deployment of learning skills in universities;use of artificial intelligence to avoid errors in referring a football match;and extracting and exploiting the behavior business process graph through transition-centric event-log data.

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Innovative Poverty Estimation through machine learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

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18th IEEE international conference on Emerging Technologies, ICET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 IEEE.

关键词： Forecasting

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Multi-branch Person Re-identification Net 2

Multi-branch Person Re-identification Net

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2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Chen, Zhihao Ge, Yiyuan Zhang, Ji Gao, Xiang Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Xihua University School of Mechanical Science and Engineering Chengdu China

ISBN: (纸本)9798350331417

Person ReID is a complex and important problem within the domain of computer vision. Its objective is to identify and match individuals in video sequences, often captured from different camera viewpoints. By comparing visual characteristics, poses, and clothing of pedestrians, Person ReID enables the tracking and recognition of the same person across various scenarios. This paper introduces a Multi-Branches Person Re-identification Net (MPRN), a novel framework for person re-identification. MPRN leverages OSNet as the backbone and establishes a multi-branch structure on top of it, effectively integrating both local and global features. Experimental results demonstrate that the combination of OSNet and the multi-branch structure yields highly competitive results. Specifically, it achieves a mean average precision (mAP) of 90.5% and a rank-1 accuracy of 96% on the Market-1501 dataset, as well as an mAP of 85.5% and a rank-1 accuracy of 87.7% on the CHUK03-L dataset. © 2023 IEEE.

关键词： Feature Extraction Multi-Branch Structure Person Re-identification

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Improving Fine-tuning Pre-trained Models on Small Source Code Datasets via Variational Information Bottleneck 30

Improving Fine-tuning Pre-trained Models on Small Source Cod...

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30th IEEE international conference on software Analysis, Evolution and Reengineering (SANER)

作者： Liu, Jiaxing Sha, Chaofeng Peng, Xin Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665452786

Small datasets are common in software engineering tasks such as linguistic smell detection and code runtime complexity prediction, as crafting these datasets often involves expert knowledge. Prior work usually applies machine learning algorithms (e.g., logistic regression and SVM) with handcrafted features to tackle them, which could outperform neural models such as CNN. Recently, researchers have employed fine-tuning large pre-trained code models on various code-related tasks thanks to their transferability. However, it might be still instable and overfitting when fine-tuning on small datasets. In this paper, we firstly conduct an empirical study to fine-tune CodeBERT(a) on four code-related small datasets and observe the instability phenomenon. This could be induced by overcapacity and irrelevant features inherent in these large pre-trained code models with respective to those small datasets. To address this issue, we leverage variational information bottleneck to filter out irrelevant features when fine-tuning the models. The experiments demonstrate the out-performance of our method compared to standard fine-tuning and regularization method such as dropout and weight decay. We also experimentally study the stability of our method through varying dataset sizes. Our code and data are available at https://***/little-pikachu-hash/VIBCodeBERT.

关键词： pre-trained model fine-tuning small software engineering dataset information bottleneck

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Face2Emoji: A framework for face micro-expression recognition and emoji-packetization based on convolutional neural networks 5

Face2Emoji: A framework for face micro-expression recognitio...

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5th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Xue, Ruxin Heilongjiang University Software Engineering Heilongjiang China

ISBN: (纸本)9798350359930

Face2Emoji is an innovative framework based on convolutional neural networks (CNNs) for recognizing face micro-expressions and converting them into corresponding emoji symbols. Micro-expressions, lasting between 1/25 to 1/5 seconds, are involuntary facial muscle movements that often go unnoticed despite being genuine expressions of emotion. Despite the significant progress in the field of microexpression recognition, previous work has faced challenges in seamlessly integrating micro-expressions with expressive digital symbols such as emojis. This paper explores the potential connection between microexpressions and emoji symbols, identifying seven major human expressions and transforming them into emoji packets. The proposed framework enriches emotional expression, offering new perspectives for emotional recognition and digital communication. Cross-database experiments demonstrate the universality and scalability of the method, proving its effectiveness and superiority. © 2023 IEEE.

关键词： Convolution

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A Novel Method for Detecting Faults Using Automation in Power Systems with machine learning 4

A Novel Method for Detecting Faults Using Automation in Powe...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Nigam, Manoj Kumar Deshmukh, Shailesh M. Mishra, Ankit Mishra, Rahul Kalinga University Department of Electrical Engineering Naya Raipur India Amity University Department of Electrical Engineering Raipur India Kalinga University Naya Raipur India

ISBN: (纸本)9798350393248

In this we discussed about the impact of mutual inductance on the dynamic modeling of Switched Reluctance (SR) motors and their performance. COMSOL software is used to simulate the performance of an 8/6 pole SR motor. Here, we also examine how well the redesigned stator pole SR motor performs in comparison, taking into account the impact of mutual inductance. The inductance profile and magnetic flux profile at various rotor positions have been displayed by the simulation results. © 2023 IEEE.

关键词： Inductance

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