Supriyo Bandyopadhyay, Ph.D.

Commonwealth Professor, Department of Electrical and Computer Engineering VCU College of Engineering

  • Richmond VA

Professor Bandyopadhyay has authored and co-authored over 400 research publications

Contact

VCU College of Engineering

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Biography

Supriyo Bandyopadhyay is Commonwealth Professor of Electrical and Computer Engineering at Virginia Commonwealth University. He received a B. Tech degree in Electronics and Electrical Communications Engineering from the Indian Institute of Technology, Kharagpur, India; an M.S degree in Electrical Engineering from Southern Illinois University, Carbondale, Illinois; and a Ph.D. degree in Electrical Engineering from Purdue University, West Lafayette, Indiana. He spent one year as a Visiting Assistant Professor at Purdue University, West Lafayette, Indiana (1986-87) and then nine years on the faculty of University of Notre Dame. In 1996, he joined University of Nebraska-Lincoln as Professor of Electrical Engineering, and then in 2001, moved to Virginia Commonwealth University as a Professor of Electrical and Computer Engineering, with a courtesy appointment as Professor of Physics. He directs the Quantum Device Laboratory in the Department of Electrical and Computer Engineering. Research in the laboratory has been frequently featured in national and international media. Its educational activities were highlighted in a pilot study conducted by the ASME to assess nanotechnology pipeline challenges. The laboratory has graduated many outstanding researchers who have won numerous national and international awards.

Prof. Bandyopadhyay has authored and co-authored over 400 research publications and presented over 150 invited or keynote talks at conferences and colloquia/seminars across five continents. He is the author of three popular textbooks, including the only English language textbook on spintronics. He is currently a member of the editorial boards of ten international journals and served in the editorial boards of ten others in the past. He has served in various committees of ~100 international conferences and workshops. He is the founding Chair of the Institute of Electrical and Electronics Engineers (IEEE) Technical Committee on Spintronics and past-chair of the Technical Committee on Compound Semiconductor Devices and Circuits. He was an IEEE Electron Device Society Distinguished Lecturer (2005-2012) and an IEEE Nanotechnology Council Distinguished Lecturer (2016, 2017). He is a past Vice President of the IEEE Nanotechnology Council in charge of conferences (2006-2007) and later in charge of publications (2020-2022). Prof. Bandyopadhyay is the winner of many awards and distinctions.

Industry Expertise

Education/Learning
Research

Areas of Expertise

Self-assembly of Regimented Nanostructure Arrays
Spintronics
Quantum Devices
Hot Carrier Transport in Nanostructures
Nanoelectronics
Quantum Computing
Nanomagnetism
Computing Paradigms
Optical Properties of Nanostructures
Coherent spin transport in Nanowires for Sensing and Information Processing
Nanowire-based Room Temperature Infrared Detectors

Accomplishments

University Award of Excellence

2017-08-23

Virginia Commonwealth University faculty award for performing in a superior manner in teaching, scholarly activity and service. One award is given to one faculty member in the University in any year. It is one of the highest awards the University can bestow on a faculty member. Dr. Bandyopadhyay is the only recipient of this award in the history of the College of Engineering.

Virginia's Outstanding Scientist

2016-02-15

Named by the Governor of the State of Virginia, 2016. One of two recipients in the State of Virginia in 2016. This award is given across all fields of engineering, science, mathematics and medicine.

Electrical and Computer Engineering Lifetime Achievement Award, VCU

Department of Electrical and Computer Engineering, Virginia Commonwealth University, 2015. One of two such awards given in the department's history.

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Education

Purdue University

Ph.D.

Electrical Engineering

Southern Illinois University

M.S.

Electrical Engineering

Indian Institute of Technology, Kharagpur

B.Tech

Electronics and Electrical Communications Engineering

Affiliations

  • American Physical Society
  • The Electrochemical Society
  • American Association for the Advancement of Science
  • Institute of Electrical and Electronics Engineers: Past Vice President of Nanotechnology Council, Past Associate Editor of IEEE Transactions on Electron Devices, Past Chair of the Technical Committee on Compound Semiconductor Devices and Circuits, Founding Chair of the Technical Committee on Spintronics
  • Institute of Physics (UK): Editorial Board Member of the journals Nanotechnology and Nano Futures

Media Appearances

Gov. Northam recognizes Outstanding Faculty Award recipients

Augusta Free Press  print

2018-03-02

Supriyo Bandyopadhyay is commonwealth professor of electrical and computer engineering at Virginia Commonwealth University where he has worked for 17 years as director of the Quantum Device Laboratory. Bandyopadhyay was named Virginia’s Outstanding Scientist by Governor Terry McAuliffe in 2016.

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Governor Northam recognizes outstanding faculty awards recipients

Virginia Secretary of Education  online

2018-03-01

RICHMOND - Governor Ralph Northam today recognized 12 Virginia educators as recipients of the 32nd annual Outstanding Faculty Award for excellence in teaching, research, and public service. The annual Outstanding Faculty Award program is administered by the State Council of Higher Education for Virginia (SCHEV) and sponsored by Dominion Energy.

“These outstanding educators have devoted their lives to research and teaching.” said Governor Northam. “Each has a proven track record of academic excellence and giving back to their communities. I am pleased to support these wonderful Virginia teachers and it is my privilege to recognize each of them with the Outstanding Faculty Award.”

The recipients, all faculty members from colleges and universities across the Commonwealth, were honored today during an awards ceremony at the Jefferson Hotel in Richmond.

“The 12 educators that we are recognizing play a pivotal role in the lives and successes of the people they teach and inspire,” said Secretary of Education Atif Qarni. “With this award we thank them for their service to students, to their institutions, and to the Commonwealth.”

“We are fortunate that Virginia is home to one of the world’s great systems of higher education,” said Peter Blake, director of SCHEV. “The Outstanding Faculty Awards recognize faculty members who have dedicated their lives to research, teaching, and mentorship. Their work improves the lives of everyone in the Commonwealth.”

The awards are being made through a $75,000 grant from the Dominion Energy Charitable Foundation, the philanthropic arm of Dominion Energy and the sponsor of the Outstanding Faculty Awards for the 14th year.

“Dominion Energy is pleased to partner with SCHEV once again to honor Virginia’s outstanding educators,” said Hunter A. Applewhite, president of the Dominion Energy Charitable Foundation. “Every year, I am impressed and humbled by the dedication shown by these teachers and researchers to guide and inspire our young people to excel in the classroom and in life.”

VCU Engineering Professor receives Governor's highest award for Teaching

Virginia Commonwealth University  online

2018-02-07

Supriyo Bandyopadhyay, Ph.D., Commonwealth Professor in the Virginia Commonwealth University School of Engineering, has been named a recipient of the 2018 State Council of Higher Education for Virginia (SCHEV) Outstanding Faculty Award

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Research Focus

Spintronics

Nanostructures

2017-01-03

Spintronics is the science and technology of storing, sensing, processing and communicating information with the quantum mechanical spin properties of electrons.

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Straintronics

Nanomagnets

2017-01-03

Straintronics is the technology of rotating the magnetization direction of nanomagnets with electrically generated mechanical stress. It has applications in extremely energy-efficient Boolean and non-Boolean computing.

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Infrared photodetection

Nanowires

2017-01-03

Infrared photodetectors have applications in night vision, collision avoidance systems, healthcare, mine detection, monitoring of global warming, forensics, etc. Room temperature detection of infrared light is enabled via quantum engineering in nanowires and by exploiting spin properties of electrons.

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Patents

Magneto-elastic non-volatile multiferroic logic and memory with ultralow energy dissipation

9379162

2016-06-28

Memory cells, non-volatile logic gates, and combinations thereof have magneto-tunneling junctions (MTJs) which are switched using potential differences across a piezoelectric layer in elastic contact with a magnetostrictive nanomagnet of an MTJ. One or more pairs of electrodes are arranged about the MTJ for supplying voltage across the piezoelectric layer for switching. A permanent magnetic field may be employed to change the positions of the stable magnetic orientations of the magnetostrictive nanomagnet. Exemplary memory cells and universal non-volatile logic gates show dramatically improved performance characteristics, particularly with respect to energy dissipation and error-resilience, over existing methods and architectures for switching MTJs such as spin transfer torque (STT) techniques.

Room temperature nanowire IR, visible and UV photodetectors

8946678

2015-02-03

Room temperature IR and UV photodetectors are provided by electrochemical self-assembly of nanowires. The detectivity of such IR detectors is up to ten times better than the state of the art. Broad peaks are observed in the room temperature absorption spectra of 10-nm diameter nanowires of CdSe and ZnS at photon energies close to the bandgap energy, indicating that the detectors are frequency selective and preferably detect light of specific frequencies. Provided is a photodetector comprising: an aluminum substrate; a layer of insulator disposed on the aluminum substrate and comprising an array of columnar pores; a plurality of semiconductor nanowires disposed within the pores and standing vertically relative to the aluminum substrate; a layer of nickel disposed in operable communication with one or more of the semiconductor nanowires; and wire leads in operable communication with the aluminum substrate and the layer of nickel for connection with an electrical circuit.

Planar multiferroic/magnetostrictive nanostructures as memory elements, two-stage logic gates and four-state logic elements for information processing

8921962

2014-12-30

A magnetostrictive-piezoelectric multiferroic single- or multi-domain nanomagnet whose magnetization can be rotated through application of an electric field across the piezoelectric layer has a structure that can include either a shape-anisotropic mangnetostrictive nanomagnet with no magnetocrystalline anisotropy or a circular nanomagnet with biaxial magnetocrystalline anisotropy with dimensions of nominal diameter and thickness. This structure can be used to write and store binary bits encoded in the magnetization orientation, thereby functioning as a memory element, or perform both Boolean and non-Boolean computation, or be integrated with existing magnetic tunneling junction (MTJ) technology to perform a read operation by adding a barrier layer for the MTJ having a high coercivity to serve as the hard magnetic layer of the MTJ, and electrical contact layers of a soft material with small Young's modulus.

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Research Grants

Non-volatile Ultra-Low-Power Magnetic Non-Binary Matrix Multipliers as Hardware Accelerators for Machine Learning and Artificial Intelligence

Virginia Innovation Partnership Corporation $75000

2023-01-01

To commercialize a non-binary all-spin matrix multiplier for deep learning tasks via customer discovery.

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Non-binary all spin matrix multiplier

VCU Commercialization Fund $15000

2023-03-01

To commercialize an all spin matrix multiplier for deep learning and artificial intelligence via specific prototype fabrication.

Ultralow-power straintronic switch implemented with a nanomagnet and a topological insulator for “processor in memory” architectures

Virginia Microelectronics Consortium $33000

2023-06-01

To demonstrate a novel switch implemented with a nanomagnet possessing perpendicular magnetic anisotropy deposited on a topological insulator thin film deposited on a piezoelectric substrate. When the nanomagnet is strained with a voltage applied to the piezoelectric, its anisotropy changes to in-plane and exchange interaction opens up a gap in the topological insulator and changes its resistivity.

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Courses

EGRE 620: Electron Theory of Solids

Introduces graduate students to quantum theory of solids with emphasis on applications in solid state devices.

EGRE 621: Introduction to Spintronics

Introduces advanced graduate students to various facets of spintronics, spin physics, spin devices and elements of spin based quantum computing.

EGRE 610: Research Practices in Electrical and Computer Engineering

Introduces graduate students to grant writing, paper writing and perfects their skills in oral presentations.

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Selected Articles

Roadmap for unconventional computing with nanotechnology

Nano Futures, 8, 012001 (2024)

Giovanni Finocchio, et al.

2024-03-28

In the 'Beyond Moore's Law' era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

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Magnetic Straintronics for Ultra-Energy-Efficient Unconventional Computing: A Review

IEEE Transactions on Magnetics, 9, 4100110 (2024)

Supriyo Bandyopadhyay

2024-05-22

With rapidly increasing edge intelligence, domain-specific computers in heterogeneous fabrics are likely to rule the roost. Judicious choice of device technology and computational paradigms can drastically reduce the size, weight, and power (SWaP) of such computers, while also making them fully autonomous (clockless) and resilient against malicious attacks. Here, we review the promise of an emerging device technology—magnetic straintronics—in implementing extremely energy efficient hardware for a wide variety of computing paradigms: neuromorphic, probabilistic, Bayesian belief networks, Boltzmann (BM) and Ising machines (IMs), matrix multipliers for deep learning networks, and reconfigurable stochastic neurons for p-computing. Magnetic straintronics has two important features—non-volatility and very low energy expenditure—which are conducive to edge processing and hardware cybersecurity. We discuss some unconventional computing paradigms implemented with magnetic straintronics while pointing out the remarkable energy efficiency in all cases.

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Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Nanotechnology, 35, 325205 (2024)

Rahnuma Rahman, Samiran Ganguly, Supriyo Bandyopadhyay

2024-05-23

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as
combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

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