| LDR | | 04137cmm u22008058i 4500 |
| 001 | | 000000340798 |
| 003 | | EBZ |
| 005 | | 20260127112846 |
| 006 | | m d |
| 007 | | cr ||||||||||| |
| 008 | | 250617s2026 flua o 000 0 eng d |
| 015 | |
▼a GBC576490
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| 016 | 7 |
▼a 300582198
▼2 Uk |
| 020 | |
▼a 9781040398104
▼q ePub ebook |
| 020 | |
▼a 1040398103 |
| 020 | |
▼a 9781003508304
▼q (electronic bk.) |
| 020 | |
▼a 1003508308
▼q (electronic bk.) |
| 020 | |
▼a 1040398057
▼q (electronic bk. : PDF) |
| 020 | |
▼a 9781040398050
▼q (electronic bk.) |
| 020 | |
▼z 9781032796888 |
| 020 | |
▼z 103279688X |
| 024 | 8 |
▼a CIPO000252455 |
| 024 | 7 |
▼a 10.1201/9781003508304
▼2 doi |
| 037 | |
▼a 9781003508304
▼b Taylor & Francis |
| 040 | |
▼a EBZ
▼b eng
▼c EBZ
▼d 248032 |
| 049 | |
▼a MAIN |
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▼a Q325.5 |
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▼a 621.381520285631
▼2 23/eng/20250807 |
| 245 | 00 |
▼a Machine learning for semiconductor materials /
▼c edited by Neeraj Gupta, Rashmi Gupta, Rekha Yadav, Sandeep Dhariwal, Rajkumar Sarma. |
| 260 | |
▼a Boca Raton :
▼b CRC Press,
▼c 2026. |
| 300 | |
▼a 1 online resource (206 pages) :
▼b illustrations (black and white). |
| 336 | |
▼a text
▼2 rdacontent |
| 336 | |
▼a still image
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▼a computer
▼2 rdamedia |
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▼a online resource
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▼a textual
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▼3 EBSCOhost |
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▼a Emerging materials and technologies |
| 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. |
| 532 | 0 |
▼3 EBSCOhost
▼a "EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025. |
| 588 | |
▼a Description based on CIP data; resource not viewed. |
| 590 | |
▼a Added to collection customer.56279.3 |
| 650 | 0 |
▼a Machine learning
▼x Industrial applications. |
| 650 | 0 |
▼a Semiconductors. |
| 650 | 7 |
▼a semiconductor.
▼2 aat |
| 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 | 08 |
▼i Print version:
▼z 9781032796888 |
| 776 | 08 |
▼i Print version:
▼z 103279688X
▼z 9781032796888 |
| 856 | 40 |
▼3 EBSCOhost
▼u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=4229012 |
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▼n 4229012 |
| 990 | |
▼a 관리자 |