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Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits

AutorGuo, Xinjie; Merrihk-Bayat, F.; Gao, Ligand; Hoskins, Brian D.; Alibart, Fabien; Linares-Barranco, Bernabé ; Theogarajan, Luke; Teucher, Christof; Strukov, Dmitri B.
Fecha de publicación2015
EditorFrontiers Media
CitaciónFrontiers in Neuroscience 9 art. 488 (2015)
ResumenThe 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.
Versión del editorhttp://dx.doi.org/10.3389/fnins.2015.00488
URIhttp://hdl.handle.net/10261/128393
DOI10.3389/fnins.2015.00488
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