Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non–von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.

Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2–Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2) (refs. 4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications.

Research and development efforts in the nonvolatile memory arena are focused on a reduced set of innovative components, among which we can include memristors. [1,2] Memristors are expected to be key players in the electronics landscape of the coming years largely because of the powerful applications that stand upon their unique features. [1,[3][4][5] The switching mechanisms behind memristors differ significantly depending on the physical properties of the structures and the materials involved. [1,[3][4][5][6][7] To list some of these mechanisms, we can highlight those devices based on phase-change materials, which can be switched reversibly between amorphous and crystalline phases with different electrical resistivity (phasechange memories, PCMs); [8] devices that take advantage of the magnetic and electrical properties exhibited by some materials with different architectures (magnetic RAMs, MRAMs); [9] also structures where materials with switchable electrical polarization give rise to hysteresis curves of the polarization versus electrical field that can be engineered for storing information (ferroelectric FET, FFET); [10] and, finally, resistive RAMs (RRAMs) where the dielectric conduction properties are altered by means of the internal ion movement and concurrent redox reactions used to generate different resistive states. [1,3,11,12]

The development of artificial neural networks using memristors is gaining a lot of interest among technological companies because it can reduce the computing time and energy consumption. There is still no memristor, made of any material, capable to provide the ideal figures-of-merit required for the implementation of artificial neural networks, meaning that more research is required. Here we present the use of multilayer hexagonal boron nitride based memristors to implement spiking neural networks for image classification. Our study indicates that the recognition accuracy of the network is high, and that can be resilient to device variability if the number of neurons employed is large enough. There are very few studies that present the use of a two-dimensional material for the implementation of synapses of different features; in our case, in addition to a study of the synaptic characteristics of our memristive devices, we deal with complete spiking neural network training and inference processes.

An in-depth analysis of resistive switching (RS) in unipolar devices is performed by means of a new simulator based on resistive circuit breakers of different features. The forming, set and reset processes are described in terms of the stochastic formation and rupture of conductive filaments of several branches in the dielectric. Both, the electric field and temperature dependencies are incorporated in the simulation. The simulation tool was tuned with experimental data of devices fabricated making use of the Ti/HfO2/Si stack. The variability and the stochastic behavior are characterized and reproduced correctly by simulation to understand the physics behind RS. Reset curves with several current steps are explained considering the rupture of different branches of the conductive filament. The simulation approach allows to connect in a natural manner to compact modeling solutions for the devices under study.