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portada Machine Learning for Semiconductor Materials
Type
Physical Book
Publisher
Collection
Emerging Materials and Technologies
Year
2025
Pages
208
Format
Hardcover
Dimensions
23.4x15.6 cm
ISBN13
9781032796888

Machine Learning for Semiconductor Materials

Neeraj Gupta;Sandeep Dhariwal;Rekha Yadav;Rashmi Gupta;Rajkumar Sarma (Author) · CRC Press · Hardcover

Machine Learning for Semiconductor Materials - Neeraj Gupta;Sandeep Dhariwal;Rekha Yadav;Rashmi Gupta;Rajkumar Sarma

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Synopsis "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 the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth.

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 the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices, analog and Radio Frequency (RF) behaviour of semiconductor devices with different materials.

Features:

Focuses on the 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 device at high frequency. Explores pertinent biomolecule detection method. Reviews recent methods in the field of machine learning for semiconductor materials with real-life application. Examines 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.

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