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Fabia Farlin Athena

Postdoctoral Fellow at Stanford University

November 17, 2025 at 4:00 PM PT -> my local time Add to Calendar

Location: Rm 786, Davis Hall, Berkeley     Watch Live on Zoom / YouTube

Adaptive Engineering of Heterogeneous Materials and Devices for 3D Energy-Efficient Electronics poster

Abstract

Data-intensive computing, exemplified by artificial intelligence (AI), is reshaping our lives, from healthcare to home security, but at a steep energy cost. In modern computing, most energy is spent moving data rather than processing it, creating an energy bottleneck known as the memory wall. Overcoming this challenge is crucial to enabling the energy-efficient electronics required for sustainable AI. Approaches such as analog in-memory computing and high-density, 3D on-chip memory offer the potential to overcome this bottleneck. However, realizing these approaches requires innovation in the fundamental building blocks of hardware, which, in turn, demands advances in device and materials engineering, including transport physics, interfaces, and integration. I will discuss how adaptive engineering leverages heterogeneous materials and devices to meet device-specific requirements and thus enables energy-efficient electronics. I will present key examples from my work. The first involves optimizing transport, interfacial, and thermal properties to enable adaptive oxide-based, low-power, non-volatile resistive random access memories for deep learning applications and exploit bias-induced phase transitions. The second leverages interface dipole engineering, monolithic 3D integration, and nanoscale process innovation for amorphous oxide semiconductor (AOS) two-transistor (2T) gain cell memory. Finally, I will conclude by outlining my vision, which combines adaptive engineering of novel materials and devices, 3D integration, and sustainable manufacturing to achieve unprecedented energy efficiency in computing hardware.

Speaker Bio

Fabia Farlin Athena is an Energy Postdoctoral Fellow in Electrical Engineering at Stanford University, advised by Prof. H.-S. Philip Wong and Prof. Alberto Salleo, studying high density on-chip oxide-semiconductor gain-cell memories and monolithic 3D memory-logic stacks for ultra-low-power AI hardware. She earned her PhD and MS in Electrical and Computer Engineering from Georgia Institute of Technology, advised by Prof. Eric M. Vogel, where her research focused on adaptive oxide-based resistive memories for deep learning applications, receiving the Sigma Xi Best PhD Thesis 2025. She has also held research scientist intern roles at IBM T. J. Watson Research Center. Athena’s research has been recognized by the IBM PhD Fellowship, MRS Graduate Student Award, Cadence Technology Scholarship, VLSI Symposium Highlight Paper, EECS and MSE Rising Stars, Stanford Energy Postdoctoral Fellowship, and Forbes 30 Under 30 North America.

Details for upcoming seminars will be posted soon.