The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.

In memristors and resistance switching devices, there is a region prior to switching which exhibits current transients with potentially useful dynamics. We refer to this region as the subthreshold region owing to it occurring prior to any switching threshold. These transients exhibit a characteristic peaked response with a fast rise in current followed by a slower decay. This behaviour has previously been used to quantify the mobilities of defects drifting within the active layer of the devices, but it has also been used in neuromorphic circuits to carry out edge detection, to implement homeostasis within artificial synapses and could have uses in replicating eligibility traces. We present an empirical SPICE model to reproduce these transients within circuit simulators. The model is compared with experimental datasets for a range of applied voltages and we present experimentally verified parameters for readers to use within their own simulations.

Digital computers have been getting exponentially faster for decades, but huge challenges exist today. Transistor scaling, described by Moore's law, has been slowing down over the last few years, ending the era of fully predictable performance improvements. Furthermore, the data-centric computing demands fueled by machine learning applications are rapidly growing, and current computing systems -- even with the historical rate of improvements driven by Moore's law -- cannot keep up with these enormous computational demands. Some are turning to analogue in-memory computing as a solution, where specialised systems operating on physical principles accelerate specific tasks. We explore how emerging nonvolatile memories can be used to implement such systems tailored for machine learning. In particular, we discuss how memristive crossbar arrays can accelerate key linear algebra operations used in neural networks, what technological challenges remain, and how they can be overcome.

A design concept of phase-separated amorphous nanocomposite thin films is presented that realizes interfacial resistive switching (RS) in hafnium-oxide-based devices. The films are formed by incorporating an average of 7% Ba into hafnium oxide during pulsed laser deposition at temperatures ≤400°C. The added Ba prevents the films from crystallizing and leads to ∼20-nm-thin films consisting of an amorphous HfOx host matrix interspersed with ∼2-nm-wide, ∼5-to-10-nm-pitch Ba-rich amorphous nanocolumns penetrating approximately two-thirds through the films. This restricts the RS to an interfacial Schottky-like energy barrier whose magnitude is tuned by ionic migration under an applied electric field. Resulting devices achieve stable cycle-to-cycle, device-to-device, and sample-to-sample reproducibility with a measured switching endurance of ≥104 cycles for a memory window ≥10 at switching voltages of ±2 V. Each device can be set to multiple intermediate resistance states, which enables synaptic spike-timing–dependent plasticity. The presented concept unlocks additional design variables for RS devices.

We present a resistance switching device that exhibits analogue potentiation and depression of conductance under the same voltage polarity. This contrasts with previously studied devices that potentiate and depress under opposite polarities. We refer to this mode of operation as the subthreshold regime due to it occurring at voltage or current biases that are insufficient to produce discrete or non-volatile switching. This behaviour has the potential to reduce the complexity of neuronal and synaptic circuitry in neuromorphic computing by removing the need for voltage pulses of both positive and negative polarities. The characteristically long timescales may also help replicate bio-realistic timings. In this article, we detail how to induce this unique behaviour, how to tune its properties to a desired response, and finally, we demonstrate one potential application.

In this study we present a resistance switching device that exhibits analogue potentiation and depression of conductance in the same polarity. This is in contrast to devices studied previously which exhibit potentiation and depression in opposite polarities. This has the potential to reduce complexity of the surrounding circuitry in neuromorphic computing by only requiring voltage pulses of a single polarity. In this paper, we detail how to induce this unique behaviour in devices as well as how to tune its properties to a desired response.

Filamentary resistance switching, or ReRAM, devices based on oxides suffer from device-do-device and cycle-to-cycle variability of electrical characteristics (electroforming voltages, set and reset voltages, resistance levels and cycling endurance). These are largely materials issues related to the microstructure of the switching oxide. Here we outline strategies to engineer the electrical performance of silicon oxide ReRAM by controlling the oxide microstructure at the nanometre scale through approaches including engineered interfaces and ion implantation. We demonstrate control over the distribution of switching voltages, electroforming voltages, and stable multilevel resistance states.

We report that implanting argon ions into a film of uniform atomic layer deposition (ALD)-grown SiOx enables electroforming and switching within films that previously failed to electroform at voltages <15 V. We note an implantation dose dependence of electroforming success rate: electroforming can be eliminated when the dosage is high enough. Our devices are capable of multi-level switching during both set and reset operations, and multiple resistance states can be retained for more than 30,000 s under ambient conditions. High endurance of more than 7 million (7.9 × 106) cycles is achieved alongside low switching voltages (±1 V). Comparing SiOx fabricated by this approach with sputtered SiOx we find similar conduction mechanisms between the two materials. Our results show that intrinsic SiOx switching can be achieved with defects created solely by argon bombardment; in contrast to defects generated during deposition, implantation generated defects are potentially more controllable. In the future, noble ion implantation into silicon oxide may allow optimization of already excellent resistance switching devices.