Filamentary TaOx/HfO2 ReRAM Devices for Neural Networks Training with Analog In‐Memory Computing

The in‐memory computing paradigm aims at overcoming the intrinsic inefficiencies of Von‐Neumann computers by reducing the data‐transport per arithmetic operation. Crossbar arrays of multilevel memristive devices enable efficient calculations of matrix‐vector‐multiplications, an operation extensively...

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Vydáno v:	Advanced electronic materials Ročník 8; číslo 10
Hlavní autoři:	Stecconi, Tommaso, Guido, Roberto, Berchialla, Luca, La Porta, Antonio, Weiss, Jonas, Popoff, Youri, Halter, Mattia, Sousa, Marilyne, Horst, Folkert, Dávila, Diana, Drechsler, Ute, Dittmann, Regina, Offrein, Bert Jan, Bragaglia, Valeria
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	01.10.2022
Témata:	analog memory artificial synapses HfO 2 resistive random‐access memory TaO x
ISSN:	2199-160X, 2199-160X
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Popis
Shrnutí:	The in‐memory computing paradigm aims at overcoming the intrinsic inefficiencies of Von‐Neumann computers by reducing the data‐transport per arithmetic operation. Crossbar arrays of multilevel memristive devices enable efficient calculations of matrix‐vector‐multiplications, an operation extensively called on in artificial intelligence (AI) tasks. Resistive random‐access memories (ReRAMs) are promising candidate devices for such applications. However, they generally exhibit large stochasticity and device‐to‐device variability. The integration of a sub‐stoichiometric metal‐oxide within the ReRAM stack can improve the resistive switching graduality and stochasticity. To this purpose, a conductive TaOx layer is developed and stacked on HfO2 between TiN electrodes, to create a complementary metal‐oxide‐semiconductor‐compatible ReRAM structure. This device shows accumulative conductance updates in both directions, as required for training neural networks. Moreover, by reducing the TaOx thickness and by increasing its resistivity, the device resistive states increase, as required for reduced power consumption. An electric field‐driven TaOx oxidation/reduction is responsible for the ReRAM switching. To demonstrate the potential of the optimized TaOx/HfO2 devices, the training of a fully‐connected neural network on the Modified National Institute of Standards and Technology database dataset is simulated and benchmarked against a full precision digital implementation. A conductive TaOx material is developed to create a complementary metal‐oxide‐semiconductor‐compatible bilayer TaOx/HfO2 resistive random‐access memory. This device is based on filamentary switching mechanisms, but shows reduced stochasticity and improved graduality compared to metal–insulator–metal baselines. Applying short (<200 ns) and low amplitude (<1.5 V) voltage pulses the device responds with quasi‐analog conductance updates in both directions.
ISSN:	2199-160X 2199-160X
DOI:	10.1002/aelm.202200448