In the race of fabricating solid‐state nano/microelectronic devices using 2D layered materials (LMs), achieving high yield and low device‐to‐device variability are the two main challenges. Electronic devices that drive currents in‐plane and homogeneously along the 2D‐LMs (i.e., transistors, memtransistors) are strongly affected by local defects (i.e., grain boundaries, wrinkles, thickness fluctuations, polymer residues), as they create inhomogeneities and increase the device‐to‐device variability, resulting in a poor performance at the circuit level. Here, it is shown that memristors are insensitive to most types of defects in 2D‐LMs, even when fabricated in academic laboratories that do not meet industrial standards. The reason is that the currents produced in these devices, which flow out‐of‐plane across the 2D‐LM, are always driven locally by the most conductive locations. Consequently, it is concluded that it is much easier to fabricate 2D‐LMs‐based solid‐state nano/microelectronic circuits using memristors than using transistors or memtransistors, not only due to the inherent simpler fabrication process (i.e., less lithography steps) but also because the local defects do not degrade the yield and variability of memristors considerably.

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.

Brain-inspired computing enabled by memristors has gained prominence over the years due to the nanoscale footprint and reduced complexity for implementing synapses and neurons. The demonstration of complex neuromorphic circuits using conventional materials systems has been limited by high cycle-to-cycle and device-to-device variability. Two-dimensional (2D) materials have been used to realize transparent, flexible, ultra-thin memristive synapses for neuromorphic computing, but with limited knowledge on the statistical variation of devices. In this work, we demonstrate ultra-low-variability synapses using chemical vapor deposited 2D MoS2 as the switching medium with Ti/Au electrodes. These devices, fabricated using a transfer-free process, exhibit ultra-low variability in SET voltage, RESET power distribution, and synaptic weight update characteristics. This ultra-low variability is enabled by the interface rendered by a Ti/Au top contact on Si-rich MoS2 layers of mixed orientation, corroborated by transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), and X-ray photoelectron spectroscopy (XPS). TEM images further confirm the stability of the device stack even after subjecting the device to 100 SET–RESET cycles. Additionally, we implement logic gates by monolithic integration of MoS2 synapses with MoS2 leaky integrate-and-fire neurons to show the viability of these devices for non-von Neumann computing.

2D materials have many outstanding properties that make them attractive for the fabrication of electronic devices, such as high conductivity, flexibility, and transparency. However, integrating 2D materials in commercial devices and circuits is challenging because their structure and properties can be damaged during the fabrication process. Recent studies have demonstrated that standard metal deposition techniques (like electron beam evaporation and sputtering) significantly damage the atomic structure of 2D materials. Here it is shown that the deposition of metal via inkjet printing technology does not produce any observable damage in the atomic structure of ultrathin 2D materials, and it can keep a sharp interface. These conclusions are supported by abundant data obtained via atomistic simulations, transmission electron microscopy, nano-chemical metrology, and device characterization in a probe station. The results are important for the understanding of inkjet printing technology applied to 2D materials, and they could contribute to the better design and optimization of electronic devices and circuits.

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.