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

Fabia Farlin Athena

Fabia Farlin Athena

Energy Postdoctoral Fellow

Stanford University

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 Artificial Intelligence (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.

Interests
  • Oxide Semiconductors
  • Low Dimensional Materials
  • Emerging Memories
  • Energy Efficient AI
  • Device Physics
  • Analog AI
Education
  • PhD in Electrical and Computer Engineering, May, 2024

    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

 
 
 
 
 
Stanford University
Energy Postdoctoral Fellow
2024 – Present Stanford, CA
 
 
 
 
 
Georgia Institute of Technology
IBM PhD Fellow
2022 – 2024 Atlanta, GA
 
 
 
 
 
IBM T. J. Watson Research Center
Research Scientist Intern
2023 – 2023 Yorktown Heights, NY
 
 
 
 
 
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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Understanding the Mechanism of Adaptive Oxide Memristors
(i) Investigation into the dynamics of Ti/HfOx synapses via modeling - I developed a compact model based on Trap-Assisted Tunneling and Ohmic transport principles to understand the working principle of adaptive oxide memristors. The model replicated experimental analog resistance change and temperature-driven current-voltage relationships up to ~200°C. This model also enabled simulations of analog pulse impacts on rupture thickness and extraction of barrier-height parameters at the oxide/electrode interface. We observed barrier height impacts the current responses. Consequently, it is anticipated that the active layer dopant will affect the barrier height and, thereby, the resistance change. [Applied Physics Letters]
(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]
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. [ACS Applied Electronic Materials]
(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]
Improving the Performance of Adaptive Oxide Memristors
Deep Learning Application of Adaptive Oxide Memristors
(i) Recovery of Deep Learning Performance of a degraded array - To examine the practical applications of adaptive oxide-based memristors in deep learning, I collaborated with IBM T. J. Watson Research Center. We demonstrate ReSta—a novel biasing technique. Designed to rejuvenate the filaments of fatigued memristive arrays after repeated training, ReSta significantly boosts long-term neural network accuracy on analog AI hardware. Next, we leveraged open loop training without write verification on this hardware with 98% accuracy. [IEEE Transactions on Electron Devices]
(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]
Deep Learning Application of Adaptive Oxide Memristors
2D Indium (III) Selenide-based Transistors and Memristors
In2Se3, with its unique electronic and optical properties, can be a potential game-changer in the semiconductor arena, benefiting from its high electron mobility and bandgap tunability. To understand the electrical responses, I have been working on In2Se3 based on two and three-terminal devices. I have developed a fabrication process for three-terminal 𝛽-In2Se3 based back-gated transistors with only two-step lithography. We are currently continuing to uncover the underlying mechanisms and impact of substrate and growth conditions on the electrical properties of these devices.

Publications

(2024). Monolithic 3D integration of FeFET array for in-memory compute. .Patent approved for filing.

(2024). First Demonstration of an N-P Oxide Semiconductor Complementary Gain Cell Memory. In IEEE International Electron Devices Meeting (IEDM) 2024.

Cite

(2024). MAX Phase Ti2AlN for HfO2 Memristors with Ultra‐Low Reset Current Density and Large On/Off Ratio. In Advanced Functional Materials.

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(2024). Demonstration of Transfer Learning Using 14 nm Technology Analog ReRAM Array. In Frontiers in Electronics.

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(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 (IEDM) 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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Featured Media Articles

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.
  • I would like to especially encourage students from underrepresented groups to reach out.

Favourite Quotes

  • “The scientific equations we seek are the poetry of nature.” –Prof. Chen Ning Yang

  • “Success is not final, failure is not fatal: it is the courage to continue that counts.” –Winston S. Churchill