Andreeva Natalia V, Ryndin Eugeny A, Mazing Dmitriy S, Vilkov Oleg Y, Luchinin Victor V
Department of Micro- and Nanoelectronics, Faculty of Electronics, Saint Petersburg State Electrotechnical University "LETI", Saint Petersburg, Russia.
Department of Solid State Electronics, Saint Petersburg State University, Saint Petersburg, Russia.
Front Neurosci. 2022 Jun 14;16:913618. doi: 10.3389/fnins.2022.913618. eCollection 2022.
In this paper, we report an approach to design nanolayered memristive compositions based on TiO/AlO bilayer structures with analog non-volatile and volatile tuning of the resistance. The structure of the TiO layer drives the physical mechanism underlying the non-volatile resistance switching, which can be changed from electronic to ionic, enabling the synaptic behavior emulation. The presence of the anatase phase in the amorphous TiO layer induces the resistive switching mechanism due to electronic processes. In this case, the switching of the resistance within the range of seven orders of magnitude is experimentally observed. In the bilayer with amorphous titanium dioxide, the participation of ionic processes in the switching mechanism results in narrowing the tuning range down to 2-3 orders of magnitude and increasing the operating voltages. In this way, a combination of TiO/AlO bilayers with inert electrodes enables synaptic behavior emulation, while active electrodes induce the neuronal behavior caused by cation density variation in the active AlO layer of the structure. We consider that the proposed approach could help to explore the memristive capabilities of nanolayered compositions in a more functional way, enabling implementation of artificial neural network algorithms at the material level and simplifying neuromorphic layouts, while maintaining all benefits of neuromorphic architectures.
在本文中，我们报告了一种基于TiO/AlO双层结构设计纳米层忆阻组合物的方法，该结构具有电阻的模拟非易失性和易失性调节。TiO层的结构驱动了非易失性电阻切换背后的物理机制，这种机制可以从电子型转变为离子型，从而实现突触行为模拟。非晶TiO层中锐钛矿相的存在由于电子过程而引发电阻切换机制。在这种情况下，通过实验观察到电阻在七个数量级范围内的切换。在具有非晶二氧化钛的双层结构中，离子过程参与切换机制导致调节范围缩小至2 - 3个数量级，并提高了工作电压。这样，TiO/AlO双层与惰性电极的组合能够实现突触行为模拟，而活性电极则会引发由结构中活性AlO层中阳离子密度变化所导致的神经元行为。我们认为，所提出的方法有助于以更具功能性的方式探索纳米层组合物的忆阻能力，能够在材料层面实现人工神经网络算法并简化神经形态布局，同时保留神经形态架构的所有优势。
