The electrical characteristics and resistive switching properties of memristive devices have been studied in a wide temperature range. The insulator and electrode materials of these devices (silicon oxide and titanium nitride, respectively) are fully compatible with conventional complementary metal-oxide-semiconductor (CMOS) fabrication processes. Silicon oxide is also obtained through the low-temperature chemical vapor deposition method. It is revealed that the as-fabricated devices do not require electroforming but their resistance state cannot be stored before thermal treatment. After the thermal treatment, the devices exhibit bipolar-type resistive switching with synaptic behavior. The conduction mechanisms in the device stack are associated with the effect of traps in the insulator, which form filaments in the places where the electric field is concentrated. The filaments shortcut the capacitance of the stack to different degrees in the high-resistance state (HRS) and in the low-resistance state (LRS). As a result, the electron transport possesses an activation nature with relatively low values of activation energy in an HRS. On the contrary, Ohm’s law and tunneling are observed in an LRS. CMOS-compatible materials and low-temperature fabrication techniques enable the easy integration of the studied resistive-switching devices with traditional analog–digital circuits to implement new-generation hardware neuromorphic systems.

It is shown that two modes of resistive switching – bipolar and volatile unipolar – are peculiar for the Ag/Ge/Si structures with germinating dislocations in the germanium layer. In this modes the structures have stable states of electric current with ION/IOFF ~1.5–2.7. The volatile unipolar type of switching can be caused by the capture of charge carriers to deep levels associated with lattice defects in the Ge film of the memristor. At the same time, bipolar switching is associated with the drift of Ag+ ions along germinating dislocations.

Computing in memory represented by resistive switching (memristive) devices is a promising way to implement hardware for artificial neural networks. We see it as the way to create energy-efficient and high-performance systems for solving a wide range of tasks in artificial intelligence. Here, we present the structure and performance of our crossbar array based on Au/Ta/ZrO₂(Y)/Pt/Ti memristive devices, integrated into a complementary metal–oxide–semiconductor fabrication process, and then demonstrate its applicability for the hardware implementation of artificial neural networks. The array contains 256 memristive devices combined into 32 columns and eight rows. Each memristive device is paired with an n-channel metal–oxide–semiconductor transistor. As the results of the simulations show, these devices enable running neural network algorithms for solving recognition problems using the Modified National Institute of Standards and Technology dataset with 98% accuracy and the Canadian Institute for Advanced Research (10 classes) dataset with at least 77.5% accuracy, even taking into account the errors of mapping weights onto the memristive crossbar. Fully hardware recognition of 4 bit images demonstrated in this work highlights the crucial role of selective transistors for such tasks. This paper explores the exciting potential of memristor-based components of neuroelectronics as the building blocks for a new era of artificial intelligence hardware, capable of surpassing current computational limitations, as well as their role in the ongoing transition to hybrid intelligence based on the symbiosis of electronic and biological systems.