Atomistic Origins of Conductance Switching in an ε-Cu<sub>0.9</sub>V<sub>2</sub>O<sub>5</sub> Neuromorphic Single Crystal Oscillator
Building artificial neurons and synapses is key to achieving the promise of energy efficiency and acceleration envisioned for brain-inspired information processing. Emulating the spiking behavior of biological neurons in physical materials requires precise programming of conductance nonlinearities. Strong correlated solid-state compounds exhibit pronounced nonlinearities such as metal-insulator transitions arising from dynamic electron-electron and electron-lattice interactions. However, a detailed understanding of atomic rearrangements and their implications for electronic structure remains obscure. In this work, we unveil discontinuous conductance switching from an antiferromagnetic insulator to a paramagnetic metal in ε-Cu<sub>0.9</sub>V<sub>2</sub>O<sub>5</sub>. Distinctively, fashioning nonlinear dynamical oscillators from entire millimeter-sized crystals allows us to map the structural transformations underpinning conductance switching at an atomistic scale using single-crystal X-ray diffraction. We observe superlattice ordering of Cu ions between [V<sub>4</sub>O<sub>10</sub>] layers at low temperatures, a direct result of interchain Cu-ion migration and intrachain reorganization. The resulting charge and spin ordering along the vanadium oxide framework stabilizes an insulating state. Using X-ray absorption and emission spectroscopies, assigned with the aid of electronic structure calculations and measurements of partially and completely decuprated samples, we find that Cu 3<i>d</i> and V 3<i>d</i> orbitals are closely overlapped near the Fermi level. The filling and overlap of these states, specifically the narrowing/broadening of V 3<i>d</i><sub><i>xy</i></sub> states near the Fermi level, mediate conductance switching upon Cu-ion rearrangement. Understanding the mechanisms of conductance nonlinearities in terms of ion motion along specific trajectories can enable the atomistic design of neuromorphic active elements through strategies such as cointercalation and site-selective modification.
Authors
- Sarbajit Banerjee (ORCID: https://orcid.org/0000-0002-2028-4675)
- George Agbeworvi (ORCID: https://orcid.org/0000-0002-2126-1854)
- S. Chakraborty (ORCID: https://orcid.org/0000-0002-2758-2069)
- John D. Ponis (ORCID: https://orcid.org/0000-0001-5385-2301)
- G. Sambandamurthy (ORCID: https://orcid.org/0000-0002-3946-9629)
- Patrick J. Shamberger (ORCID: https://orcid.org/0000-0002-8737-6064)
- Cherno Jaye (ORCID: https://orcid.org/0000-0001-6158-6373)
- Jinghua Guo (ORCID: https://orcid.org/0000-0002-8576-2172)
- Katherine Jungjohann (ORCID: https://orcid.org/0000-0002-8132-1230)
- Michelle A. Smeaton (ORCID: https://orcid.org/0000-0001-9114-1009)
- Saul Perez-Beltran (ORCID: https://orcid.org/0000-0002-3403-2225)
- Daniel A. Fischer (ORCID: https://orcid.org/0000-0002-3072-6000)
- Conan Weiland (ORCID: https://orcid.org/0000-0001-6808-1941)
- Lu‐Fang Ma (ORCID: https://orcid.org/0000-0002-5601-6437)
- Edrick Wang
- Xiaofeng Qian (ORCID: https://orcid.org/0000-0003-1627-288X)
- Fatme Jardali (ORCID: https://orcid.org/0000-0001-6959-0430)
- Nitin Kumar (ORCID: https://orcid.org/0000-0002-1064-1659)
- Kenna Ashen (ORCID: https://orcid.org/0009-0000-4647-6328)
- Jialu Li
- Nicholas Jerla
Institutions
- National Laboratory of the Rockies (US)
- National Institute of Standards and Technology (US)
- Lawrence Berkeley National Laboratory (US)
- Brookhaven National Laboratory (US)
- Material Measurement Laboratory (US)
- University at Buffalo, State University of New York (US)
- Texas A&M University (US)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1021/jacs.4c11968
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- National Science Foundation
- Basic Energy Sciences