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This book gives insight into the emerging semiconductor devices from their applications in electronic circuits. It discusses the challenges in the field of engineering and applications of advanced low-power devices. Emerging Low-Power Semiconductor Devices: Applications for Future Technology Nodes offers essential exposure to low-power devices, and applications in wireless, biosensing, and circuit domains. This book provides a detailed discussion on all aspects, including the current and future scenarios related to the low-power device. The book also presents basic knowledge about field-effect transistor (FET) devices and introduces emerging and novel FET devices. The chapters include a review of the usage of FET devices in various domains like biosensing, wireless, and cryogenics applications. The chapters also explore device-circuit co-design issues in the digital and analog domains. The content is presented in an easy-to-follow manner that makes it ideal for individuals new to the subject. This book is intended for scientists, researchers, and postgraduate students looking for an understanding of device physics, circuits, and systems.
Role of TFET Dand Their Performance Analysis for Wireless Communications
Modeling and Simulation of Emerging Low-Power Devices
Tunnel-Field-Effect Transistor: An Energy-Efficient Semiconductor Device
Analytical Modeling of Surface Potential of a Double-Gate Heterostructure PNPN Tunnel FET
Impact of Semiconductor Materials and Architecture Design on TFET Device Performance
Performance Analysis of Emerging Low-Power Junctionless Tunnel FETs
Recent Developments in Schottky Diodes and Their Applications
Numerical Study of Symmetric Underlap S/D High-ĸ Spacer on JAM-GAA FinFET for Low-Power Applications
Potential Prospects of Negative Capacitance Field Effect Transistors for Low-Power Applications
Memory Designing Using Low-Power FETs for Future Technology Nodes
TFET-Based Flash Analog-to-Digital Converter
Demand of Low-Power-Driven FET as Biosensors in Biomedical Applications
2D Materials for Spin Orbital Torque MRAM: A Path toward Neuromorphic Computing