Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2018
Publisher DOI: 10.1142/S0218126617501833
Title: Integrated circuit with memristor emulator array and neuron circuits for biologically inspired neuromorphic pattern recognition
Language: English
Authors: Ranjan, Rajeev 
Mendoza Ponce, Pablo 
Hellweg, Wolf Lukas 
Kyrmanidis, Alexandros 
Abu Saleh, Lait 
Schroeder, Dietmar 
Krautschneider, Wolfgang H. 
Keywords: ASIC;emulator;CMOS;LTD;LTP;memristor;neuron;synaptic plasticity;pattern recognition
Issue Date: 27-Apr-2017
Publisher: World Scientific Publishing
Source: Journal of Circuits, Systems and Computers 11 (26): 1750183 (2017)
Journal or Series Name: Journal of circuits, systems, and computers 
Abstract (english): This paper details an application-specific integrated circuit (ASIC) with an array of switched-resistor-based memristors (resistor with memory) and integrate & fire (I & F) neuron circuits for the development of memristor-based pattern recognition. Since real memristors are not commercially available, a compact memristor emulator is needed for device study. The designed ASIC has five memristor emulators with one having a conductance range from 4.88ns to 4.99μs (200kOhm) to 204.8MOhm)) and other four having conductance ranging from 195ns to 190μs (5.2kOhm) to 5.12MOhm)). Signal processing has been planned to be off-chip to get the freedom of programmability of a wide range of memristive behavior. This paper introduces the memristor emulator and the realization of synapse functionalities used in neuromorphic circuits such as long term potentiation (LTP), Long Term depression (LTD) and synaptic plasticity. The ASIC has two I & F neuron circuits which are intended to be used in conjunction with memristors in a multiple chip network for pattern recognition. This paper explains the memristor emulator, I & F neuron circuit and a respective neuromorphic system for pattern recognition simulated in LTspice. The ASIC has been fabricated in AMS 350nm process.
URI: http://hdl.handle.net/11420/2021
DOI: 10.15480/882.2018
ISSN: 0218-1266
Institute: Integrierte Schaltungen E-9 
Type: (wissenschaftlicher) Artikel
Funded by: Deutsche Forschungsgemeinschaft (DFG)
Project: C 2 (Neural Circuits) 
Appears in Collections:Publications (tub.dok)

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