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Integrated Circuits (ICs) are the foundation of modern electronics, enabling complex functionalities to be embedded into compact, high-performance devices. VLSI (Very Large-Scale Integration) technology allows millions to billions of transistors to be fabricated on a single chip, powering everything from smartphones and computers to advanced medical equipment and communication systems.
At the Academy of Technology (AOT), research in Semiconductor Devices and CAD (Computer-Aided Design) for VLSI focuses on both the physical and design aspects of microelectronic systems. On the device side, faculty and students study semiconductor physics, fabrication techniques, and emerging materials such as gallium nitride (GaN) and silicon carbide (SiC) to improve performance, efficiency, and thermal stability. Research also explores nanoscale device modeling, low-power design strategies, and the integration of novel transistor architectures like FinFETs and gate-all-around (GAA) devices. In the domain of CAD for VLSI, AOT develops advanced tools and algorithms for chip design, simulation, verification, and optimization. Emphasis is placed on reducing design cycle time, improving accuracy, and meeting constraints such as power consumption, speed, and area efficiency. Researchers also work on design automation for mixed-signal and system-on-chip (SoC) architectures, integrating hardware and software co-design methodologies.
Applications of this research span consumer electronics, automotive electronics, telecommunications, and defense systems. For example, low-power VLSI designs enable longer battery life in portable devices, while high-performance ICs drive data-intensive applications like AI and high-speed networking.
AOT’s research is supported by well-equipped microelectronics and VLSI design laboratories with industry-standard EDA (Electronic Design Automation) tools. Collaborations with semiconductor companies and research institutes ensure alignment with current technological trends and industry demands. Future research directions at AOT include exploring quantum device integration, neuromorphic computing hardware, and 3D IC architectures to meet the performance needs of next-generation computing systems.
Problem Description: The growing demand for sustainable energy and advanced photonic technologies drives the need for efficient, stable, and scalable optoelectronic devices. This research integrates machine learning (ML) with physics-based design to optimize devices like perovskite and heterojunction solar cells (PSC/HSC), light-emitting diodes (LEDs), and avalanche photodiodes (APDs). ML models enable rapid performance prediction and structural optimization, overcoming limitations of traditional simulation methods. The goal is to develop a synergistic framework that enhances quantum efficiency, power conversion, and stability, while supporting green energy initiatives and next-generation applications in photovoltaics, sensing, communication, and computing through the improved design of photonic and optoelectronic platforms.
Students: Aninda Chandra, Atriya Set, Debajit Kumar, Niladri Burman, Lokenath Sarkar
Supervisors: Dr. Kanishka Majumder, Subham Pramanik (ECE Department)
Year: 2025-26
Students: Sushovan Dey, Supriyo Chatterjee, Soumojit Ghosh, Debasish
Sarkar, Swastik Das
Supervisor: Dr. Kanishka Majumder (ECE Department)
Year: 2025-26
Students: Atia Khatun, Anuska Chandra, Aditya Jha, Dhruba Mondal
Supervisors: Dr. Kanishka Majumder, Subham Pramanik (ECE Department), Subhashis Das (CSE Department)
Year: 2025-26
Students: Debjyoti Karmakar, Joydip Saw, Debashis Modak, Subhajit Pramanick, Suman Pal
Supervisor: Dr. Kanishka Majumder (ECE Department)
Year: 2024-25
Students: Dipayan Chatterjee, Swarupa Das, Sudeshna Kundu, Sowradeep Pal, Suman Panja
Supervisor: Dr. Kanishka Majumder (ECE Department)
Year: 2024-25
Students: Barsa Das, Poulami De, Piasa Badopadhyay, Priyanka De, Piyali Ghosh
Supervisor: Dr. Kanishka Majumder (ECE Department)
Year: 2018-19
Problem Description: The primary challenge in reversible circuit testing is not only the detection of faults but also the accurate diagnosis of the identified faults within the circuit. Faults in reversible circuits can disrupt logical operations, making efficient detection and localization essential for reliable functioning. To address this, several algorithms have been proposed that focus on identifying and localizing different types of faults. These include single missing gate fault (SMGF), repeated gate fault (RGF), partial missing gate fault (PMGF), and multiple missing gate fault (MMGF). Each type presents unique testing difficulties, and specialized techniques are essential to ensure accurate fault detection and diagnosis.
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(+91) 9831021706
(+91) 9830161441
academy@aot.edu.in
placement@aot.edu.in
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