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Automated Vickers hardness system with closed-loop control for polishing pads for semiconductor production

Instrumentation science and Technology 2024年第4期52卷 401-414页

作者： Huy, Le Nam Quoc Hoa, Le Ngoc Quynh Haq, Muhamad Amirul Huynh, Tan-Thanh Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Nanyang Technological University Singapore Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan School of Applied Chemistry Tra Vinh University Viet Nam

Chemical mechanical polishing plays a pivotal role in enhancing the topography of wafers during semiconductor fabrication. The hardness of polishing pads holds great significance as it determines the material removal rate during the planarization process. Hence, precise determination of polishing pad hardness at both micro and macro scales is essential for optimizing the semiconductor polishing process, ensuring the prevention of wafer erosion or dishing. In this study, an automated system was developed for determining Vickers hardness of microporous polishing pads employing a closed-loop control method. The system was calibrated based on the relationship between load-unloading force and displacement observed in Vickers indentation experiments. The designed apparatus is not only successfully verified with standard values for Vickers reference blocks with less than 2% error, but it also demonstrated a smaller average relative error percentage than the commercial system using traditional open-loop measurement method. The closed-loop Vickers hardness apparatus proves to be a promising alternative for measuring hardness accurately, particularly in samples with noisy surfaces, rough surfaces, and porous microstructures. © 2023 Taylor & Francis Group, LLC.

关键词： Fabrication

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Assessment of processing time and measurement accuracy of different phase compensation methods in Quantitative Phase Imaging via Digital Holographic Microscopy applied to Biological Specimens Holographic Microscopy

Assessment of processing time and measurement accuracy of di...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied optics group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States

Six different methods for phase compensation in Digital Holographic Microscopy are compared using a calibrated test target and a Toxocara canis larva sample regarding processing time, measurement accuracy, and usefuln...

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Structured Illumination Digital Holographic Microscopy via two integrated Mach-Zehnder interferometers

Structured Illumination Digital Holographic Microscopy via t...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Restrepo, René Trujillo, Carlos Doblas, Ana Applied optics group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States

We propose two Match-Zehnder interferometers coupled to create a structured illumination digital holographic microscope with tunable modulation frequency capability, expanding the system's numerical aperture regar... 详细信息

关键词： Mach-Zehnder interferometers

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Towards Non-Invasive Swallowing Assessment: An AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation 46

Towards Non-Invasive Swallowing Assessment: An AI-Powered In...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Luo, Yuewen Anwar, Ayman Ren, Siyi Coyle, James L. Sejdic, Ervin University of Toronto Division of Engineering Science Faculty of Applied Science & Engineering TorontoON Canada University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software's superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment. © 2024 IEEE.

关键词： User centered design

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Prediction Based Reversible Data Hiding for Gray-Scale Images 7

Prediction Based Reversible Data Hiding for Gray-Scale Image...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Uyyala, Ravi Bojjagani, Sriramulu Department of Computer Science And Engineering Hyderabad Gandipet India School of Engineering And Applied Sciences SRM-AP Department of Computer Science And Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350371406

Predicting the pixel is crucial in prediction error expansion (PEE) based reversible data hiding (RDH). There are a number of methods for predicting pixels that can be found in research papers. The gradients are used to make a prediction about the current pixel. Gradients in the image can be used to make forecasts about the current pixel, and this has been the subject of extensive study. In this work, we suggest a three-by-three grid of surrounding pixels as a basis for prediction. Later, the histogram bin shifting technique was implemented to integrate more information with less distortion. In order to include more information, the suggested technique makes adaptive changes to the histogram based on local complexity. The experimental analysis demonstrates that When compared to the other, the proposed method is superior. © 2023 IEEE.

关键词： Pixels

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Accident Severity Prediction using Machine Learning: A case study on the US Accidents Dataset 17

Accident Severity Prediction using Machine Learning: A case ...

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17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023

作者： Zohra, Ennaji Fatima Maryam, Knouzi Hamada, El Kabtane Hasna, Elalaoui Elabdallaoui National School of Applied Science Khouribga Morocco National School of Applied Science Marrakech Morocco Uca Computer Science Engineering Laboratory Faculty of Sciences Semlalia Marrakech Morocco

ISBN: (纸本)9798350370911

In recent years, road accidents have caused numerous victims, injuries, and deaths every minute, not to mention economic damages. Due to the enormous number of traffic accidents, understanding and predicting their severity becomes a necessity. In this work, the 'US Accidents (2016 - 2023)' dataset has been used. The data exploration task was applied before the training phase. Subsequently, and after splitting data into training and testing subsets, experiments were conducted using ANN(Artificial Neural Network), Random Forest Classifier, and Logistic Regression. We achieved an accuracy of 81.1 %, 90.7 %;and 87.03 % respectively. © 2023 IEEE.

关键词： Forecasting

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

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Cardiac Arrest Prediction Model Using Ensemble Technique 4

Cardiac Arrest Prediction Model Using Ensemble Technique

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sinha, Dibakar More, Sarla Jeyakrishnan, V. Mishra, Durgesh Manipal University Jaipur Department of Computer Science Engineering Rajasthan Jaipur India Symbiosis University of Applied Sciences School of Computer Science and Information Technology MP Indore India

ISBN: (纸本)9798331518981

The physician employs a diverse range of laboratory diagnostics, physical examinations, and on occasion, even invasive procedures to ascertain the presence of diseases. It is necessary to have a physician that possesses exceptional training, expertise and skill in the specific field, for detection of the disease in its initial phases. Despite the widespread use of ensemble techniques in disease prediction, here is a notable absence of comprehensive evaluation of regularly utilized ensemble algorithms for highly studied diseases. Consumers typically resort to seeking medical assistance from a doctor or other medical expert only after exhausting all other home remedies without finding relief for their ailments. This is probably a result of insufficient comprehension, as well as other factors related to behavior. This paper introduces a conventional ensemble LSTM model that incorporates data preparation and feature engineering. The model proposed in this research represents major gains in numerous statistical parameters like precision, f score, accuracy and ROC curve in comparison to the other models. © 2024 IEEE.

关键词： Diseases

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Leveraging Semantic Parsing using Text Embeddings and Reinforcement Learning

Leveraging Semantic Parsing using Text Embeddings and Reinfo...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Piza-Londono, Jessenia Andrade-Lotero, Edgar J. Universidad Del Rosario Applied Mathematics and Computer Science Bogotá Colombia Universidad Del Rosario School of Engineering Science and Technology Bogotá Colombia

ISBN: (纸本)9798350374575

Representing natural language information is a key challenge in Artificial Intelligence and Cognitive science, requiring the transformation of unstructured data into formats suitable for computational tasks. While logical formalisms offer robust methods for information representation, their complexity often limits widespread adoption. Conversely, transformer architectures provide strong generalization capabilities but struggle with logical inference tasks. To address both the need for generalization and reliable logical inference, we propose a novel approach using deep reinforcement learning, enabling agents to autonomously learn the rules of semantic parsing. Our preliminary results indicate successful generation of appropriate representations for simple queries. Future work will extend the environment to handle a wider range of real-world sentences. © 2024 IEEE.

关键词： Deep reinforcement learning

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