TY - JOUR
T1 - Characterisation & modelling of perovskite-based synaptic memristor device
AU - Gupta, Vishal
AU - Lucarelli, Giulia
AU - Castro-Hermosa, Sergio
AU - Brown, Thomas
AU - Ottavi, Marco
N1 - Funding Information:
We thank the funding agencies, University of Rome Tor Vergata 's “Mission: Sustainability” “BiCVision” project, LazioInnova Gruppi di Ricerca project no. 85-2017-15373 SIROH , and Departamento del Huila's Scholarship Program from Huila, Colombia, under grant 677 for financial support. We would also like to thank the anonymous reviewers whose insightful comments helped to improve this manuscript.
Funding Information:
We thank the funding agencies, University of Rome Tor Vergata's ?Mission: Sustainability? ?BiCVision? project, LazioInnova Gruppi di Ricerca project no. 85-2017-15373 SIROH, and Departamento del Huila's Scholarship Program from Huila, Colombia, under grant 677 for financial support. We would also like to thank the anonymous reviewers whose insightful comments helped to improve this manuscript.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/8
Y1 - 2020/8
N2 - 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 (CH3NH3PbI3) 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/CH3NH3PbI3/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/CH3NH3PbI3/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.
AB - 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 (CH3NH3PbI3) 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/CH3NH3PbI3/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/CH3NH3PbI3/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.
KW - Low power device
KW - Memristor
KW - Perovskite
KW - SPICE modelling
KW - Synapse
UR - http://www.scopus.com/inward/record.url?scp=85085917555&partnerID=8YFLogxK
U2 - 10.1016/j.microrel.2020.113708
DO - 10.1016/j.microrel.2020.113708
M3 - Article
AN - SCOPUS:85085917555
SN - 0026-2714
VL - 111
JO - Microelectronics reliability
JF - Microelectronics reliability
M1 - 113708
ER -