Paper Info
Title
Perovskite based Low Power Synaptic Memristor Device for Neuromorphic application
Abstract
In artificial intelligence, high speed neuromorphic computing architectures are needed to perform various operations such as learning, transferring information, and processing of data. Due to high power dissipation, high operating energy, and lower density of integration CMOS device has limited application in neuromorphic computing in nanoscale domain. On the other hand memristor devices are promising candidates for implementing synaptic devices in a neuromorphic computing architecture due to their swift information storage, high-speed processing of data and high density with lower power consumption. To the best of our knowledge this paper proposes the first studies made on a perovskite $\left( C H _ { 3 } N H _ { 3 } P b I _ { 3 } \right)$ based photovoltaic memristive device with $I T O / S n O _ { 2 } / C H _ { 3 } N H _ { 3 } P b I _ { 3 } / A u$ structure in the dark condition. This perovskite based memristor is able to mimic the neuromorphic learning and remembering process same as the biological synapses. The proposed synaptic memristor device has potential to operate at low energy, low cost, solution processability, low activation energy, high efficiency and used as a power-on-chip synaptic device in artificial neural network.
Year
DOI
Venue
2019
10.1109/DTIS.2019.8734983
2019 14th International Conference on Design & Technology of Integrated Systems In Nanoscale Era (DTIS)
Keywords
Field
DocType
Memristors,Synapses,Neurons,Neuromorphics,Gold,Substrates
Topology,Memristor,Dissipation,Perovskite,Computer science,Neuromorphic engineering,High density,Electronic engineering,CMOS,Artificial neural network,Photovoltaic system
Conference
ISBN
Citations 
PageRank 
978-1-7281-3424-6
1
0.37
References 
Authors
0
5
Name
Order
Citations
PageRank
Vishal Gupta184.94
Giulia Lucarelli210.37
Sergio Castro3213.79
Thomas Brown410.37
Marco Ottavi516124.21