Noble‐Metal‐Free Memristive Devices Based on IGZO for Neuromorphic Applications

Abstract

technology is everywhere. Currently, it is the Von Neumann's architecture that is applied to the electronic computing systems in which the different elements (memory, processor, and controller) are separated. [1] Nearly all the circuits within the memory and the processor are composed of complementary metal-oxidesemiconductor (CMOS) devices, which can be a problem once this technology cannot be further miniaturized without compromising its performance. [2] A practical example could be that in order to increase the operating frequency and the device density, which is necessary when downscaling, the power supply and the operation temperature would also increase, which would obviously degrade the system performance. Moreover, the Von Neumann system is not suited to solve real world problems where inputs and outputs are sometimes not specified [3] or to execute adaptive learning algorithms as it would be necessary in tasks, such as classification of unstructured data or pattern recognition. [4] To overcome the Von Neumann's bottleneck, the development of artificial intelligence technologies and new computer architecture designs are necessary. One of such novel approaches is neuromorphic computation, which operates with extremely low power consumption. It also maintains the massive parallelism found in the human brain [5] where neurons communicate information by electrical or chemical input signals passing through synapses. Synapses present a very important behavior called plasticity which consists in changing their strength (synaptic weight), either facilitating or inhibiting the connection between two neurons, through potentiation and depression, respectively. [6] One of the solutions to emulate biological synapses is the resistive switching (RS) device, or memristor. A memristor is basically a nonlinear two-terminal device whose conductance can be altered by external inputs and depends on the history of current that has flowed through the device. Application of both weight programming and weight processing signals are through its input terminals thus mimicking a synapse. In fact, the memristor can simulate the synapses' plasticity by continuously adapting its resistance into excitatory and inhibitory weights upon application of electrical Amorphous indium-gallium-zinc-oxide (a-IGZO) based memristive devices with molybdenum contacts as both top and bottom electrodes are presented aiming to be used in neuromorphic applications. Devices down to 4 µm 2 are fabricated using conventional photolithography processes, with an extraordinary yield of 100%. X-ray photoelectron spectroscopy and transmission electron microscopy performed on the developed structures confirm the presence of a thin intermixed oxide layer (4-5 nm) containing Mo 6+ oxidation state at the interface with the bottom contact. This results in Schottky diodelike characteristics at the pristine state with a rectification ratio of 3 orders of magnitude. The devices have electroforming-free and area-dependent analog resistive switching properties. Temperature...

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Recent Progress in Solution‐Based Metal Oxide Resistive Switching Devices

“…As an example, retention loss that mimics the dynamics of biological memory is beneficial for artificial neural network applications. [ 345 ] Furthermore, a large on/off ratio is not a main concern for some neuromorphic applications. [ 339 ] However, low endurance is a limitation for computation and in memory logics, which require frequent alteration of the resistance states.…”

Towards Sustainable Crossbar Artificial Synapses with Zinc-Tin Oxide

“…When taking neuromorphic applications such as pattern recognition into account, it was shown that a better accuracy is achieved due to a lower random telegraphic noise of area-dependent memristors, compared to filamentary systems [11]. Besides that, by applying AOS-based material as the resistive switching material, flexible and transparent substrates such as plastic or paper can be used due to low processing temperatures and conventional patterning methods [12], which are great advantages for applications such as IoT. The most prominent AOS material in recent literature has been indium-galliumzinc oxide (IGZO) [12][13][14].…”

“…Besides that, by applying AOS-based material as the resistive switching material, flexible and transparent substrates such as plastic or paper can be used due to low processing temperatures and conventional patterning methods [12], which are great advantages for applications such as IoT. The most prominent AOS material in recent literature has been indium-galliumzinc oxide (IGZO) [12][13][14]. This material has shown synaptic operations which can be applied for both computing paradigms of deep neural networks (DNNs) and spiking neural networks (SNNs).…”