High-Performance Dynamic Feedback Control-based 8T SRAM using CNTFET Technology Abhishek Kumar, Vipin Sharma Serbian Journal of Electrical Engineering, 2026 Exploration of new materials and device technologies for integrated circuits has become essential due to the exponential growth in demand for highperformance, energy-efficient, and scalable computers. In light of their exceptional electrical and mechanical characteristics, carbon nanotube field-effect transistors (CNTFETs) have emerged as a competitive alternative to traditional Complementary Metal-Oxide-Semiconductor (CMOS) based devices. In this work, a comprehensive overview of recent advancements, challenges, and prospects concerning CNTFET-based Static Random Access Memory (SRAM) cell design. SRAM performance poses significant challenges for VLSI circuits, including power dissipation, operational speed, area efficiency, and leakage current. Technology scaling-induced short-channel effects advocate transitioning from CMOS to CNTFET-based designs. Here, we propose an SRAM design incorporating Dynamic Feedback Control (DFC) features at CMOS 22nm technology nodes. Simulation results conducted using Synopsis HSPICE demonstrate notable enhancements: a 34% reduction in average power consumption, a 95.3% decrease in leakage current, and a 71.6% improvement in delay compared to MOSFET-based SRAM cells. Moreover, energy efficiency for read/write operations improves by 99.6%, and power dissipation is enhanced by 98.5% over MOSFET-based SRAM designs.
Predictive modeling for solar footprint prediction using machine learning Abhishek Kumar Photovoltaic Materials Synthesis Characterizations and Applications, 2026 Accurate estimates of solar footprints, especially the potential energy output based on geographical, climatic, and temporal characteristics, are becoming increasingly important as solar energy systems are widely adopted. Solar footprint describes the area of a building, rooftop, or piece of land that can best accommodate solar panels and produce electricity. Machine learning (ML)-based predictive modeling is a viable way to improve solar footprint prediction, facilitating improved solar power generation planning, management, and optimization. This chapter explores the multiple ML methods for predicting solar footprints, the variables that affect them, and the uses and difficulties with accuracy. The simulation results are interpreted as the K-nearest neighbor (KNN) model is efficient in predicting with maximum accuracy in lesser training time.
FPGA-Based Hardware Accelerators for ANN and DNN Abhishek Kumar Smart Chips for Smart Devices VLSI Design for Next Generation Iot Solutions, 2026 The integration of hardware accelerator with field programmable gate array (FPFA) into a real-time system has shown to be crucial for improving neural network performance and efficiency. Hardware accelerators are specialized components made to relieve general-purpose central processing units (CPUs) of a computationally challenging task; they enhance system performance while handling specialized activities like matrix multiplications. These components expressly for real-time applications show high reliability, low latency, and deterministic behavior requirements. The usage of hardware accelerators for artificial neural networks (ANN) and deep neural networks (DNN), which are designed to function like the human brain, is covered in this chapter and implemented with Verilog programming language to construct the accelerators for ANN and DNN on the Kintex series of FPGA.
Design and Development of Braking Systems in Fuel Cell Electric Vehicles Abhishek Kumar, Harpreet Singh Bedi Hydrogen Energy Systems Advancing Sustainable Power Solutions, 2026 Most electric cars use cooperative braking systems, which combine braking force from friction with braking torque with regenerative energy, to improve energy efficiency while preserving a certain level of braking performance and driving security. To evaluate the potential benefits of integration for fuel cell electric vehicles, a performance simulator for fuel cell hybrid electric vehicles was utilized by researchers to build an environmentally, pollution-free, zero-emission car. Such concerns have pushed the production of fuel-cell cars that run on hydrogen. The fuel cell uses compressed hydrogen and oxygen from the air to produce electricity, with hydrogen serving as the main energy source. During braking, an electric battery is used as a source of backup power to assist with driving by conserving extra energy. In this work, we have examined antilock and regenerative braking technologies. The antilock braking system (ABS)–regenerative braking system (RBS) system is evaluated through simulation with MATLAB/SIMULINK software and Advanced Vehicle Simulator (ADVISOR) utilized to assess the vehicle's performance in real-world scenarios. The simulation results demonstrate that the ABS-RBS system provides superior braking performance while also recovering a significant amount of energy lost.
Preface Smart Chips for Smart Devices VLSI Design for Next Generation Iot Solutions, 2026
Smart Chips for Smart Devices: VLSI Design for Next-Generation IoT Solutions Smart Chips for Smart Devices VLSI Design for Next Generation Iot Solutions, 2026 Master the future of semiconductor technology with this essential guide, which provides the advanced circuit design strategies necessary to develop the high-performance, low-power VLSI chips that drive the modern IoT revolution. The IoT revolution has created a demand for ever-more-sophisticated and compact devices, making VLSI design a significant concern. The demand for low-power, efficient, portable, and reliable semiconductor chips forces research into developing new designs and techniques to cater to these advanced applications. This book explores the state-of-the-art field of Very-Large-Scale Integration design, emphasizing how it is used in the Internet of Things. It demonstrates strategies that combine VLSI in IoT applications, covering a wide range of subjects that are crucial for creating integrated circuits that are dependable, powerful, and high-performing. The book explores advanced circuit design, system-on-chip (SoC) architectures, and basic VLSI design ideas. Additionally, it addresses important aspects of IoT applications, like sensor integration, low power consumption, and secure communication protocols through case studies and practical applications that show how theoretical ideas are put to use in real-world settings to address the unique requirements of IoT systems. Designed for engineers, designers, and researchers, this book is an essential guide for using VLSI principles to make more creative, scalable, and effective Internet of Things solutions.
Preface Photovoltaic Materials Synthesis Characterizations and Applications, 2026
Nanosecond latency drum kit Jadhav, Kundan Manohar, Abhishek Kumar Science Engineering and Health Studies, 2022
FPGA for secured hardware & IP ownership Suman Lata Tripathi, Abhishek Kumar, Mufti Mahmud Proceedings of 2022 IEEE International Conference of Electron Devices Society Kolkata Chapter Edkcon 2022, 2022
Static Timing Analysis of Sequential Circuit with GUI Abhishek Kumar, Suman Lata Tripathi, Sandeep Dhariwal Proceedings of 2020 IEEE International Women in Engineering Wie Conference on Electrical and Computer Engineering Wiecon Ece 2020, 2020
