Machine learning for semiconductor materials
Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and...
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| Other Authors | , , , , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Boca Raton :
CRC Press,
2025.
|
| Series | Emerging materials and technologies
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781003508304 9781040398104 9781040398050 9781032796888 |
| Physical Description | 1 online zdroj (206 stran) : ilustrace. |
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| 245 | 0 | 0 | |a Machine learning for semiconductor materials / |c edited by Neeraj Gupta, Rashmi Gupta, Rekha Yadav, Sandeep Dhariwal, Rajkumar Sarma. |
| 264 | 1 | |a Boca Raton : |b CRC Press, |c 2025. | |
| 300 | |a 1 online zdroj (206 stran) : |b ilustrace. | ||
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| 490 | 0 | |a Emerging materials and technologies | |
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| 588 | |a Description based on CIP data; resource not viewed. | ||
| 520 | |a Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features: Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-making Covers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequency Explores pertinent biomolecule detection methods Reviews recent methods in the field of machine learning for semiconductor materials with real-life applications Examines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD software This book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. | ||
| 650 | 0 | |a Machine learning |x Industrial applications. | |
| 650 | 0 | |a Semiconductors. | |
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| 700 | 1 | |a Gupta, Neeraj, |e editor. | |
| 700 | 1 | |a Gupta, Rashmi, |e editor. | |
| 700 | 1 | |a Yadav, Rekha, |e editor. | |
| 700 | 1 | |a Dhariwal, Sandeep, |e editor. | |
| 700 | 1 | |a Sarma, Rajkumar, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 9781032796888 |
| 776 | 0 | 8 | |i Print version: |z 103279688X |z 9781032796888 |w (OCoLC)1502687472 |
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