AUTHOR=Guo Xinjie , Merrikh-Bayat Farnood , Gao Ligang , Hoskins Brian D. , Alibart Fabien , Linares-Barranco Bernabe , Theogarajan Luke , Teuscher Christof , Strukov Dmitri B. TITLE=Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits JOURNAL=Frontiers in Neuroscience VOLUME=9 YEAR=2015 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2015.00488 DOI=10.3389/fnins.2015.00488 ISSN=1662-453X ABSTRACT=

The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2−x/Pt memristors and CMOS integrated circuit components.