Fueling a Future in Low-Power AI through Nanoelectronics Innovation
Hi! Welcome to my website! :)
As a Stanford Energy Fellow, I focus on developing emerging memory and logic technologies using oxide semiconductors and low dimensional materials for energy-efficient AI under the mentorship of Prof. H.-S. Philip Wong and Prof. Alberto Salleo.
I obtained my Ph.D. in Electrical and Computer Engineering from Georgia Tech, advised by Prof. Eric M. Vogel. My doctoral research focused on adaptive oxide-based brain-inspired analog and in-memory computing devices. By exploring optimizations at the material, device, and system levels in collaboration with IBM, I worked toward enhancing the performance and efficiency of these devices.
My overarching work bridges nanomaterials, fundamental device physics, chip fabrication, and practical system implementations to develop technologies aimed at reducing energy consumption in AI hardware.
- Oxide Semiconductors
- Low Dimensional Materials
- Emerging Memories
- Energy Efficient AI
- Device Physics
- Analog AI
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PhD in Electrical and Computer Engineering, May, 2024
Georgia Institute of Technology
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MS in Electrical and Computer Engineering, May, 2022
Georgia Institute of Technology
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Materials Science and Engineering, 2019
Purdue University
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BSc in Materials and Metallurgical Engineering, September, 2017
Bangladesh University of Engineering and Technology
Updates
Experience
Awards
Research Projects
(ii) Exploration of the impact of active layer dopant - I explored the impact of titanium dopant on the Atomic Layer Deposited HfOx active layer by varying Hf to Ti ratios. This exploration unveiled that increased titanium doping leads to higher vacancy concentration, signified by a decrease in bonded oxygen. The results of this work have demonstrated clear links between dopant concentration and resistive switching performance. Interestingly, the lower bandgap of titanium relative to HfO2 induces an increased tunneling current and decreased forming voltage while also enhancing linearity under negative analog pulses. However, attaining linearity under positive analog pulses remains a challenging pursuit. [Journal of Materials Chemistry C], [Journal of Applied Physics]
(ii) Off-current reduction using ultrathin multi-layered low thermally conductive materials - I posited that electrodes with low thermal conductivity could localize heat in the filament, thereby enlarging the rupture oxide area and decreasing current density. While most such materials are unsuitable as electrodes, the MAX phase uniquely combines low thermal conductivity with high electrical conductivity. Because of the ultrathin multi-layered structure, the MAX phase can confine heat and act as a better oxygen reservoir for the memristor. I fabricated memristors with MAX phase electrodes, verifying the fabrication via methods such as Raman Spectroscopy, Transmission electron microscopy, and in-situ XRD. These devices displayed ultra-low reset current density, high on-off ratio, and superior endurance, emphasizing the promise of MAX phase materials in energy-efficient, high-density memory systems. [Advanced Functional Materials]
(ii) Demonstration of Transfer Learning in a 14 nm Technology chip - We also demonstrated efficient deep neural network transfer learning utilizing hardware and algorithm co-optimization in an analog HfOx based ReRAM array. We demonstrate that in deep neural network (DNN) transfer learning for image classification tasks, convergence rates can be accelerated by approximately 3.5 times by utilizing co-optimized analog ReRAM hardware and hardware-aware Tiki-Taka v2 algorithm. [Frontiers in Electronics]
Publications
Featured Media Articles
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Georgia Tech ECE Featured Graduate, Spring 2024: GT ECE Feature; FB, GT ECE; LinkedIn, GT ECE; IG, GT ECE; Twitter, GT ECE
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Stanford Energy Postdoctoral Fellowship 2024: Stanford Feature; Georgia Tech Feature; IG, GT ECE; LinkedIn, GT ECE
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MRS Graduate Student Award 2023: GT ECE Feature; Georgia Tech MSE Feature; MRS Feature, Twitter, GT MSE; FB, GT MSE; IG, GT MSE; LinkedIn, GT ECE; LinkedIn, GT MSE
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APL Machine Learning Outstanding Oral Presentation Award, MRS 2023: GT ECE Feature; Twitter, GT ECE; LinkedIn, GT ECE
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Outstanding Graduate Student Award, Georgia Tech ECE 2023: GT ECE Feature; LinkedIn, GT ECE
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EECS Rising Stars 2023: EECS Rising Stars Feature
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Cadence Scholarship 2023: Acceswire News Feature; Cadence Design Systems Feature; Georgia Tech Feature; X, GT ECE; FB, GT ECE
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IBM PhD Fellowship Award 2022-2024: IBM Official Feature; GT ECE Feature
Outreach
Virtual office hours:
- I am also hosting virtual office hours for anyone who would like my advice/thoughts on device research, PhD applications, Stanford/GT academic programs or any other topics of interest. Please schedule using this Form.
Favourite Quotes
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“The scientific equations we seek are the poetry of nature.” –Prof. Chen Ning Yang
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“Success is not final, failure is not fatal: it is the courage to continue that counts.” –Winston S. Churchill