Materials for Neuromorphic Devices
The emerging paradigm of neuromorphic computing is inspired by neural networks of the brain and based on energy-efficient hardware for information processing. To create devices that mimic what occurs in our brains’ neurons and synapses, the scientific community must overcome a fundamental molecular engineering challenge: how to design devices that exhibit controllable and energy-efficient transition between different resistive states triggered by incoming stimuli. Our work in this area is supported by the Quantum Materials for Energy Efficient Neuromorphic Computing (QMEENC) research center, an Energy Frontier Research Center headquartered at UC San Diego.
- "Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges", Axel Hoffmann, Shriram Ramanathan, Julie Grollier, Andrew D. Kent, Marcelo Rozenberg, Ivan K. Schuller, Oleg Shpyrko, Robert Dynes, Yeshaiahu Fainman, Alex Frano, Eric E. Fullerton, Giulia Galli, Vitaliy Lomakin, Shyue Ping Ong, Amanda K. Petford-Long, Jonathan A. Schuller, Mark D. Stiles, Yayoi Takamura, and Yimei Zhu (QMMEN-C collaboration), APL Mater. 10, 070904 (2022).
Transition Metals Oxides for Neuromorphic Applications
Several transition metal oxides (TMOs) have been proposed as promising resistive switching materials, i.e. systems showing tunable resistance states, in the presence of an external electrical bias. These TMOs exhibit a metal-to-insulator transition (MIT) as a function of pressure, temperature, or doping, which may be designed to mimic the behavior of neurons and synapses in the presence of stimuli. We study the properties of promising TMOs for neuromorphic applications using first principles, electronic structure calculations, and we develop models to predict the electric bias and the effect of structural distortions in driving the MIT.
- "A metallic interface between two insulating phases of La1-xSrxCoO3-δ", S. Zhang and G. Galli, Chem. Mater. 36, 2096–2105 (2024).
- "Tunable ferroelectricity in oxygen-deficient perovskites", Y. Shin and G. Galli, npj Comput. Mater. 9, 218 (2023).
- "Determining the oxygen stoichiometry of cobaltite thin films", S. Zhang, I. Chiu, M. Lee, B. Gunn, M. Feng, T. Park, Pl Shafer, A. N'Diaye, F. Rodolakis, S. Ramanathan, A. Frano, I. Schuller, Y. Takamura and G. Galli, Chem. Mater. 34, 5, 2076 (2022).
- "Cation and anion topotactic transformations in cobaltite thin films leading to Ruddlesden-Popper phases", I-Ting Chiu, Min-Han Lee, Shaobo Cheng, Shenli Zhang, Larry Heki, Zhen Zhang, Yahya Mohtashami, Pavel N. Lapa, Mingzhen Feng, Padraic Shafer, Alpha T. N'Diaye, Apurva Mehta, Jon A. Schuller, Giulia Galli, Shriram Ramanathan, Yimei Zhu, Ivan K. Schuller, and Yayoi Takamura, Phys. Rev. Mater. 5, 064416 (2021)
- "Lessons Learned from First-Principles Calculations of Transition Metal Oxides", Hien Vo, Shenli Zhang, Wennie Wang and Giulia Galli, J. Chem. Phys. 154, 174704 (2021)
- "Predicting the onset of metal-insulator transitions in transition metal oxides—a first step to design neuromorphic devices", Shenli Zhang, Hien Vo and Giulia Galli, Chem. Mater. 2021, 33, 9, 3187–3195
- "Toward materials for neuromorphic computing: understanding the metal-to-insulator transition in La1-xSrxCoO3−δ", Shenli Zhang and Giulia Galli, npj, Comput. Mater. 6, 170 (2020) [Highlight]