Title :
Digital-to-analog and analog-to-digital conversion with metal oxide memristors for ultra-low power computing
Author :
Ligang Gao ; Merrikh-Bayat, Farshad ; Alibart, Fabien ; Xinjie Guo ; Hoskins, Brian D. ; Kwang-Ting Cheng ; Strukov, Dmitri B.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California Santa Barbara, Santa Barbara, CA, USA
Abstract :
The paper presents experimental demonstration of 6-bit digital-to-analog (DAC) and 4-bit analog-to-digital conversion (ADC) operations implemented with a hybrid circuit consisting of Pt/TiO2-x/Pt resistive switching devices (also known as ReRAMs or memristors) and a Si operational amplifier (op-amp). In particular, a binary-weighted implementation is demonstrated for DAC, while ADC is implemented with a Hopfield neural network circuit.
Keywords :
Hopfield neural nets; analogue-digital conversion; digital-analogue conversion; low-power electronics; memristors; platinum; random-access storage; titanium compounds; ADC; DAC; Hopfield neural network circuit; Pt-TiO2-x-Pt; ReRAM; Si; analog-to-digital conversion; digital-to-analog conversion; hybrid circuit; metal oxide memristors; op-amp; operational amplifier; resistive switching devices; ultra-low power computing; word length 4 bit; word length 6 bit; Hopfield neural networks; Memristors; Nanoscale devices; Neurons; Noise; Switches; Voltage measurement; Analog-to-digital conversion; Digital-to-analog conversion; Hopfield neural network; ReRAM; hybrid circuits; memristor;
Conference_Titel :
Nanoscale Architectures (NANOARCH), 2013 IEEE/ACM International Symposium on
Conference_Location :
Brooklyn, NY
Print_ISBN :
978-1-4799-0873-8
DOI :
10.1109/NanoArch.2013.6623031
