In:
ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2061-2061
Abstract:
Specialized hardware for neural networks requires materials with tunable symmetry, retention and speed at low power consumption. The vast majority of memristor are based on two types of ions: either oxygen vacancy migration, in the so-called Valence Change Memories (VCM), or a metal cation, usually Ag + and Cu 2+ , in the so-called Electrochemical Metallization Cells (ECM). Despite their excellent performance, their widespread implementation in today’s integrated circuits is delayed due to the need to address cycle-to-cycle and device-to-device variabilities while circumventing electroforming and asymmetry, which are inherent issues associated to the filamentary nature of the switching mechanism. Recently, Li-ion is emerging as an alternative, given the higher diffusivity of Li + when compared to oxygen, and the ability of Li-oxides solid state conductors to accumulate and deplete lithium at the interfaces and bulk. We have recently proposed lithium titanates, originally developed as Li-ion battery anode materials, as promising candidates for memristive-based neuromorphic computing hardware [1] . In this presentation, we will discuss the non-volatile, non-filamentary bipolar resistive switching characteristics of lithium titanates compounds, Li 4+3x Ti 5 O 12 , as a function of the lithiation degree. We have employed a recently proposed strategy to overcome lithium loss during thin film deposition and finely control the final lithiation degree of the films [2] to create stoichiometrically lithiated Li 4 Ti 5 O 12 spinel phase and a highly lithiated Li 7 Ti 5 O 12 rock- salt phase memristive devices. By using ex- and in-operando spectroscopy to monitor the Lithium filling and emptying of structural positions during electrochemical measurements, we investigate the controlled formation of a metallic phase (Li 7 Ti 5 O 12 ) percolating through an insulating medium (Li 4 Ti 5 O 12 ) with no volume changes under voltage bias, thereby controlling the spatially averaged conductivity of the film device. We present a theoretical model to explain the observed hysteretic switching behavior based on electrochemical nonequilibrium thermodynamics, in which the metal-insulator transition results from electrically driven phase separation of Li 4 Ti 5 O 12 and Li 7 Ti 5 O 12 . Permittivity enhancement drives lithium ions to regions of high electric field intensity, which become metallic filaments above a critical applied bias, and the ions relax back to their low-conductivity initial state at lower voltages. One of the most striking outcomes is that the metal-insulator transition of llithium titanate can be uniquely modulated for neuromorphic computing purposes, such as control of the neural pulse train symmetry in conductance and the resistance on-to-off ratio, simply by adjusting the lithium stoichiometry and phase pattern of the films. We report ability of highly lithiated phase of Li 7 Ti 5 O 12 for Deep Neural Network applications, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li 4 Ti 5 O 12 towards Spiking Neural Network applications, due to the shorter retention and large resistance changes. Our findings pave the way for lithium oxides to enable thin-film memristive devices with adjustable symmetry and retention. References [1] J. C. Gonzalez-Rosillo, M. Balaish, Z. D. Hood, N. Nadkarni, D. Fraggedakis, K. J. Kim, K. M. Mullin, R. Pfenninger, M. Z. Bazant, and J. L. M. Rupp, Adv. Mater. 32, 1907465 (2020). [2] R. Pfenninger, M. Struzik, I. I. Garbayo, E. Stilp, and J. L. M. Rupp, Nat. Energy 4, 475 (2019).
Type of Medium:
Online Resource
ISSN:
2151-2043
DOI:
10.1149/MA2020-02312061mtgabs
Language:
Unknown
Publisher:
The Electrochemical Society
Publication Date:
2020
detail.hit.zdb_id:
2438749-6
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