Premium
Lithium‐Battery Anode Gains Additional Functionality for Neuromorphic Computing through Metal–Insulator Phase Separation
Author(s) -
GonzalezRosillo Juan Carlos,
Balaish Moran,
Hood Zachary D.,
Nadkarni Neel,
Fraggedakis Dimitrios,
Kim Kun Joong,
Mullin Kaitlyn M.,
Pfenninger Reto,
Bazant Martin Z.,
Rupp Jennifer L. M.
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201907465
Subject(s) - materials science , neuromorphic engineering , anode , lithium (medication) , electrochemistry , phase (matter) , lithium titanate , battery (electricity) , optoelectronics , artificial neural network , nanotechnology , electrode , chemical engineering , lithium ion battery , power (physics) , computer science , thermodynamics , chemistry , physics , medicine , quantum mechanics , machine learning , endocrinology , engineering
Specialized hardware for neural networks requires materials with tunable symmetry, retention, and speed at low power consumption. The study proposes lithium titanates, originally developed as Li‐ion battery anode materials, as promising candidates for memristive‐based neuromorphic computing hardware. By using ex‐ and in operando spectroscopy to monitor the lithium filling and emptying of structural positions during electrochemical measurements, the study also investigates 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. A theoretical model to explain the observed hysteretic switching behavior based on electrochemical nonequilibrium thermodynamics is presented, 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 . Ability of highly lithiated phase of Li 7 Ti 5 O 12 for Deep Neural Network applications is reported, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li 4 Ti 5 O 12 toward Spiking Neural Network applications, due to the shorter retention and large resistance changes. The findings pave the way for lithium oxides to enable thin‐film memristive devices with adjustable symmetry and retention.