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 Cu2+, 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, Li4+3xTi5O12, 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 Li4Ti5O12 spinel phase and a highly lithiated Li7Ti5O12 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 (Li7Ti5O12) percolating through an insulating medium (Li4Ti5O12) 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 Li4Ti5O12 and Li7Ti5O12. 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 Li7Ti5O12 for Deep Neural Network applications, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li4Ti5O12 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).