Characterisation & modelling of perovskite-based synaptic memristor device. (August 2020)

• Record Type: Journal Article
• Authors: Gupta, Vishal; Lucarelli, Giulia; Castro-Hermosa, Sergio; Brown, Thomas; Ottavi, Marco
• Is Part Of: Microelectronics and reliability. Volume 111(2020)
• Publication Date: 2020-08
• Subjects: Memristor -- Perovskite -- Synapse -- Low power device -- SPICE modelling; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2020.113708
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: Neuromorphic computing architectures are required to execute several operations such as forgetting and learning behaviours with high-speed data processing. Due to the rapid advancement in technology, various transistor-based devices like field-effect transistor (FET), complementary metal-oxide-semiconductor (CMOS), etc. have the limitation to perform efficiently with a higher density of integration in combination with lower energy consumption. Consequently, there is a strong necessity for creating new devices with fast information storage, high-speed data processing, high density of integration, and low operating energy. Memristors are emerging as promising candidates as the next-generation technology which contains all the above-mentioned properties. According to previous literature, a nanoscale memristive device based on methylammonium lead iodide perovskite (CH3 NH3 PbI3 ) can be fabricated and characterised as a low power synaptic device. This study proposes the behavioural modelling of a perovskite-based synaptic memristor device with Glass/indium tin oxide (ITO)/SnO2 /CH3 NH3 PbI3 /Au structure for SPICE simulation in neuromorphic applications. We report an in-depth analysis of the physical model behind the creation of the p-i-n structure, induced by the ion drift in the perovskite layer. Furthermore, a SPICE Model is proposed to reproduce the observed behaviour of fabricated Glass/ITO/SnO2 /CH3 NH3 PbI3 /Au device and is able to mimic the neuromorphic learning and remembering process, similar to biological synapses. The proposed SPICE model will foster the potential of perovskite based synaptic devices by enabling large-scale circuit-level simulations thus allowing designers to explore the potential of this new device, for example in power-on-chip approaches and in an artificial neural network. Highlights: A detailed investigation of hysteresis behaviour in perovskite (Glass/ITO/SnO2 /CH3 NH3 PbI3 /Au) device under dark conditions. A comprehensive analysis for stability & uniformity in device behaviour and dependency on scan rate for perovskite device. Proposing a memristor SPICE model for perovskite device based on its behaviour & Metal-Semiconductor-Metal type structure. Validation of proposed SPICE model by demonstrating a close match of the simulated results with the experimental results. Proposed model is able to simulate spiking behaviour and execute SRDP function for the low power neuromorphic applications.