Jayasimha Atulasimha, Ph.D. profile photo

Jayasimha Atulasimha, Ph.D.

Qimonda Professor, Department of Mechanical and Nuclear Engineering

Engineering East Hall, Room E3250, Richmond, VA, US

jatulasimha@vcu.edu

Professor Atulasimha researches multiferroic nanomagnets logic and memory

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  • Research
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Areas of Expertise

Multiferroic nanomagnets logic and memoryMagnetostrictive spintronic nanowire strain sensorNonlinear magnetostrictive piezoelectric magnetoelectric responseFabrication of MEMS devicesHybrid spintronics-straintronics for ultralow power memory logic and higher order information processingNanomagnetism: Nanoscale magnetization dynamicsSpintronics: Spin transport and manipulation in nanowires

Accomplishments

Presidential Research Incentive Program Award | professional

Awarded by Virginia Commonwealth University

Education

University of Maryland

Ph.D., Aerospace Engineering

2006

University of Maryland

M.S., Aerospace Engineering

2003

Indian Institute of Technology - Madras

B.S., Mechanical Engineering

2001

Media Appearances

Engineering researchers develop a process that could make big data and cloud storage more energy efficient Read more at: https://phys.org/news/2016-11-big-cloud-storage-energy-efficient.html#jCp

Phys.org  online

2016-11-30

"When you look at the energy reduction that this affords, it's a major change," said Jayasimha Atulasimha, Ph.D., Qimonda associate professor in the Department of Mechanical and Nuclear Engineering. "This has the potential to significantly reduce the energy consumption in switching non-volatile magnetic memory devices." Read more at: https://phys.org/news/2016-11-big-cloud-storage-energy-efficient.html#jCp

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Straintronic spin neuron may greatly improve neural computing

ECN  online

2015-07-09

The researchers, Ayan K. Biswas, Professor Jayasimha Atulasimha, and Professor Supriyo Bandyopadhyay at Virginia Commonwealth University in Richmond, have published a paper on the straintronic spin neuron in a recent issue of Nanotechnology.

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'Straintronic spin neuron' may greatly improve neural computing

Phys.org  

2015-07-08

"Researchers have proposed a new type of artificial neuron called a 'straintronic spin neuron' that could serve as the basic unit of artificial neural networks—systems modeled on human brains that have the ability to compute, learn, and adapt. Compared to previous designs, the new artificial neuron is potentially orders of magnitude more energy-efficient, more robust against thermal degradation, and fires at a faster rate. The researchers, Ayan K. Biswas, Professor Jayasimha Atulasimha, and Professor Supriyo Bandyopadhyay at Virginia Commonwealth University in Richmond, have published a paper on the straintronic spin neuron in a recent issue of Nanotechnology..."

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Non-volatile memory improves energy efficiency by two orders of magnitude

Phys.org  online

2014-09-03

By using voltage-generated stress to switch between two magnetic states, researchers have designed a new non-volatile memory with extremely high energy efficiency—about two orders of magnitude higher than that of the previous most efficient non-volatile memories. The engineers, Ayan K. Biswas, Professor Supriyo Bandyopadhyay, and Professor Jayasimha Atulasimha at Virginia Commonwealth University in Richmond, Virginia, have published their paper on the proposed non-volatile memory in a recent issue of Applied Physics Letters. Read more at: https://phys.org/news/2014-09-non-volatile-memory-energy-efficiency-magnitude.html#jCp Read more at: https://phys.org/news/2014-09-non-volatile-memory-energy-efficiency-magnitude.html#jCp

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Non-volatile memory improves energy efficiency by two orders of magnitude

Phys.org  online

2013-09-03

By using voltage-generated stress to switch between two magnetic states, researchers have designed a new non-volatile memory with extremely high energy efficiency—about two orders of magnitude higher than that of the previous most efficient non-volatile memories. The engineers, Ayan K. Biswas, Professor Supriyo Bandyopadhyay, and Professor Jayasimha Atulasimha at Virginia Commonwealth University in Richmond, Virginia, have published their paper on the proposed non-volatile memory in a recent issue of Applied Physics Letters...

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Researchers aim for energy-harvesting CPUs

EE Times  online

2011-09-01

According to Bandyopadhyay and Jayasimha Atulasimha, an assistant professor of mechanical and nuclear engineering in the VCU School of Engineering who serves as co-principal investigator on the project, this research could lead to a type of digital computing system ideal for medical devices such as processors implanted in an epileptic patient’s brain that monitor brain signals to warn of impending seizures...

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Hybrid spintronics and straintronics enable ultra-low-energy computing and signal processing

Kurzweil  online

2011-08-17

Ref.: Kuntal Roy, Supriyo Bandyopadhyay, and Jayasimha Atulasimha, Hybrid spintronics and straintronics: A magnetic technology for ultra-low-energy computing signal processing, Applied Physics Letters, 2011; [DOI:10.1063/1.3624900]

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Selected Articles

Switching dynamics of a magnetostrictive single-domain nanomagnet subjected to stress | Physical Review B

2011

The temporal evolution of the magnetization vector of a single-domain magnetostrictive nanomagnet, subjected to in-plane stress, is studied by solving the Landau-Lifshitz-Gilbert equation. The stress is ramped up linearly in time, and the switching delay, which is the time it takes for the magnetization to flip, is computed as a function of the ramp rate. For high levels of stress, the delay exhibits a nonmonotonic dependence on the ramp rate, indicating that there is an optimum ramp rate to achieve the shortest delay. For constant ramp rate, the delay initially decreases with increasing stress but then saturates, showing that the trade-off between the delay and the stress (or the energy dissipated in switching) becomes less and less favorable with increasing stress. All of these features are due to a complex interplay between the in-plane and out-of-plane dynamics of the magnetization vector induced by stress.

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Four-state nanomagnetic logic using multiferroics | Journal of Physics D: Applied Physics

2011

The authors theoretically demonstrate the implementation of a low-power 4-state universal logic gate (NOR) using a linear array of three dipole-coupled magnetostrictive-piezoelectric multiferroic nanomagnets (e.g. Ni/PZT) with biaxial magnetocrystalline anisotropy. The two peripheral nanomagnets in the array encode the 4-state input bits in their magnetization orientations and the central nanomagnet's magnetization orientation represents the output bit. Numerical simulations are performed to confirm that the 4-state output bit is the Boolean NOR function of the two 4-state inputs bits when the array reaches its ground state. A voltage pulse alternating between −0.2 and +0.2 V, applied to the piezoelectric layer of the central nanomagnet, generates alternating tensile and compressive stress in its magnetostrictive layer. This drives the array to the correct ground state where dipole interaction between the magnets ensures that the output is the NOR function of the input. For the system considered, the gate operation is executed while dissipating only ~33 000 kT (0.138 fJ) of energy.

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Proposal for an ultrasensitive spintronic strain and stress sensor | Journal of Physics D: Applied Physics

2011

We propose a spintronic strain/stress sensor capable of sensing strain with a sensitivity of ~10−13 Hz−1/2 at room temperature with an active sensing area of ~1 cm2 and power dissipation of ~1 W. This device measures stress or strain by monitoring the change in the spin-polarized current in a parallel array of free-standing nanowire spin valves when the array is subjected to compressive or tensile stress along the wires' length. Such a sensor can be fabricated using a variety of techniques involving nanolithography, self-assembly and epitaxial growth.

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