Design of an automated particle detection system for Rutherford backscattering (RBS) using LabVIEW

Oh, Kyuhak; Christenson, Michael

doi:10.1080/10739149.2018.1540430

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Oh and Christenson [44] have developed a particle detection system for Rutherford backscattering (RBS). RBS has commonly been applied in the electronics industry for material analysis, especially for inspection in semiconductors, superconductors, thin surface structures, metal and crystal orientations, metal and crystal damage, and polymers.…”

Section: B Power Electrical and Electronicsmentioning

confidence: 99%

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed many industries have been improved. Particles and impurities at any size, shape and at any condition can be detected using the advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird's nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI in particle and impurity detection, applications of AI methods in particle and impurity detection in related industries including in EBN and AI application in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

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Section: B Power Electrical and Electronicsmentioning

confidence: 99%

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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“…With the virtual instrument automation, we are able to reduce the experimental duration and get the RBS spectra, resulting in an increased experimental throughput of the accelerator. The automation had added advantages over other similar works [21,22] in versatility, availability of Hi-RBS automation and its detailed description, multiple sample ion implantation, easy reconfiguration and amenability to add other ion beam analysis experiments. The full automation software consists of standalone executable virtual instruments which provide user interactive graphical interfaces.…”

Section: Development Of Virtual Instruments For Automated Ion Implant...mentioning

confidence: 99%

Development of PC based automation of ion beam analysis and ion implantation experiments

et al. 2020

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A: This paper presents the details of a personal computer (PC) based automation of ion beam analysis and ion implantation experimental setup that has been developed and implemented in a 1.7 MV Tandetron accelerator. Hardware and software of the PC automated system were designed to automate multiple sample ion implantation, standard Rutherford Backscattering spectrometry (RBS), and High resolution Rutherford Backscattering spectrometry (Hi-RBS) experiments. The system uses in-house developed signal conditioning circuitry, isolation amplifiers, and transistor drivers along with cost-effective and off-the-shelf commercial components like a general purpose, multifunction data acquisition card, etc. A five axis goniometer is used for handling multiple samples and their automatic positioning according to the commands from the PC over RS232 interface. Key tasks carried out in the automation of system include control and mesurement of ion beam fluence, selection of backscattered particles through screening of their energies by controlling a set of +/−25 kV and −3 kV high voltage power supplies, controlled collection of backscattered yield using single and multi channel analysers, controlling the region-of-interest windows in the energy spectra and the cumulative counts in them. Controlling the position of the samples and facilitating the experiments on multiple samples, online display of energy spectra and its auto storage, and vacuum interlocking are also automatically done. This PC based setup has been automated using a windows based LabVIEW software making it easy to port the set up between different computer operating systems apart from achieving many other advantages like quick and easy development of versatile, multi-purpose, and user friendly graphical interface. Extensive tests carried out on the hardware established correct functioning of various modules of the hardware. The 0-10 V control inputs applied to the +/−25 kV and −3 kV high voltage power supplies were tested and seen providing an <0.1% accuracy, <0.05% linearity, very low drift of 50 ppm, 0.01% precision, and <1mv rms output noise. The software modules have been tested to check independent control and monitoring the irradiated dose, energy to voltage conversion, the multi channel analyser 1Corresponding author.

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Design of an automated particle detection system for Rutherford backscattering (RBS) using LabVIEW

Cited by 2 publications

References 14 publications

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Development of PC based automation of ion beam analysis and ion implantation experiments

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