Fueling a Sustainable Future in Low-Power AI through Nanomaterials Innovation

Fabia Farlin Athena

Fabia Farlin Athena

PhD Candidate

Georgia Institute of Technology

I am a PhD Candidate at Georgia Tech, majoring in Electrical and Computer Engineering. My research aims to develop a deeper understanding of neuromorphic computing for analog AI to unlock its full potential through material optimization to system-level improvements and is supported by the IBM PhD Fellowship.

The overarching directions of my research are as follows:

  • Understanding the Mechanism of Adaptive Oxide Memristors
  • Improving the Performance of Adaptive Oxide Memristors
  • Deep Learning Application of Adaptive Oxide Memristors
  • 2D In2Se3-based Transistors and Memristors

Virtual office hours:

  • I am also hosting virtual office hours for anyone who would like my advice/thoughts on device research, PhD applications, GT academic programs or any other topics of interest. Please schedule using this Form.
  • I would like to especially encourage students from underrepresented groups to reach out.
Interests
  • Nanomaterials
  • Flexible Electronics
  • Neuromorphic Computing
  • Device Physics
  • Analog AI
Education
  • PhD in Electrical and Computer Engineering, Ongoing

    Georgia Institute of Technology

  • MS in Electrical and Computer Engineering, May, 2022

    Georgia Institute of Technology

  • Materials Science and Engineering, 2019

    Purdue University

  • BSc in Materials and Metallurgical Engineering, September, 2017

    Bangladesh University of Engineering and Technology

Experience

 
 
 
 
 
Georgia Institute of Technology
PhD Fellow
2022 – Present Atlanta, GA
 
 
 
 
 
IBM T. J. Watson Research Center
Research Intern
2022 – 2022 Yorktown Heights, NY
 
 
 
 
 
Georgia Instititute of Technology
Graduate Research Assistant
2019 – 2022 Atlanta, GA
 
 
 
 
 
Purdue University
Graduate Research Assistant
2019 – 2019 West Lafayette, IN
 
 
 
 
 
Bangladesh University of Engineering and Technology
Lecturer
2018 – 2019 Dhaka, Bangladesh

Research Projects

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Improving the Performance of Adaptive Oxide Memristors
(i) Addition of barrier layer in HfOx devices - Oxygen ion dynamics influence resistance changes. Integrating a SiOx (< 1 nm) barrier layer near the reset anode interface can control this, leading to gradual resistance changes during positive analog pulses. While the HfOx/SiOx devices outperform standard ones, there’s a trade-off between linearity and switching window because of a small oxide formation in the filament. The barrier layer devices, despite better linearity, have high reset current densities, necessitating further low-power synapse investigation.
(ii) Off-current reduction using ultrathin multilayered 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 ultrathin multi layered structure MAX phase can confine heat and can act as a better oxygen reservoir for memristor. I fabricated memristors with MAX phase electrodes , verifying the fabrication via methods such as Raman Spectroscopy, Transmission electron microscopy, 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.

Publications

(2023). ReSta: Recovery of Accuracy During Training of Deep Learning Models in a 14-nm Technology-Based ReRAM Array. In IEEE Transactions on Electron Devices.

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(2023). Trade-off between Gradual Set and On/Off Ratio in HfOx-Based Analog Memory with a Thin SiOx Barrier Layer. In ACS Applied Electronic Materials.

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(2023). Thermal environment impact on HfOx RRAM operation: A nanoscale thermometry and modeling study. In Journal of Applied Physics.

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(2023). Asymmetric Resistive Switching of Bilayer HfO x/AlO y and AlO y/HfO x Memristors: The Oxide Layer Characteristics and Performance Optimization for Digital Set and Analog Reset Switching. In ACS Applied Electronic Materials.

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(2023). Bias history impacts the analog resistance change of HfOx-based neuromorphic synapses. In Applied Physics Letters.

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(2022). Deep learning acceleration in 14nm CMOS compatible ReRAM array: device, material and algorithm co-optimization. In IEEE International Electron Devices Meeting 2022.

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(2022). Impact of oxygen concentration at the HfOx/Ti interface on the behavior of HfOx filamentary memristors. In Journal of Materials Science.

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(2022). Impact of titanium doping and pulsing conditions on the analog temporal response of hafnium oxide based memristor synapses. In Journal of Applied Physics.

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(2022). Towards a better understanding of the forming and resistive switching behavior of Ti-doped HfO x RRAM. In Journal of Materials Chemistry C.

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(2018). Size Dependent Magnetic and Optical Properties of Mn Doped Bi0. 9Ho0. 1FeO3 Nanoparticles. In IOP Conference Series Materials Science and Engineering.

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(2018). Theoretical and Experimental Evidence of Modified Structure, Magnetism and Optical Properties in Ba and Mn Co-Substituted BiFeO3. In IOP Conference Series Materials Science and Engineering.

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