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 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.

This study aims to contribute to the burgeoning field of brain‐inspired computing by expanding it beyond conventional fabrication methods. Herein, the obstacles toward the effective inkjet printing process are encountered and the electrical characteristics are explored, providing new insights into the reliability aspects of fully printed Ag/a‐TiO2/Ag electronic synapses. The versatility of the approach is further enhanced by the highly stable in‐house‐developed a‐TiO2 ink, exhibiting optimal shelf life of five months and repeatable jetting, producing layers with nanoscale thickness resolution. Most importantly, device electrical characterization reveals synaptic dynamics, leading to activity‐dependent conductance state retention and adaptation characteristics, implying inherent learning capabilities. The synaptic dynamics are attained by solely adjusting the duty cycle of the applied pulsed voltage trigger, while keeping amplitude and polarity fixed, a method readily compatible with realistic applications. Furthermore, I–V analysis demonstrates a dynamic range dependence on a‐TiO2 layer thickness and conduction mechanism that is akin to the conventionally developed electronic TiO2 synapses. The developed devices provide a time‐ and cost‐effective ecologically benign alternative toward biomimetic signal processing for future flexible neural networks.

In a data‐driven economy, virtually all industries benefit from advances in information technology—powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if the discrepancy between the current computing power demands and what the existing technologies can offer is not addressed. Key limitations to improving energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal–oxide–semiconductor (CMOS) technologies, such as transistors. Herein, three approaches that will likely play an essential role in future computing systems are discussed: memristive electronics, spintronics, and electronics based on 2D materials. The authors present how these technologies may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing.

Artificial neural networks (ANNs) providing sophisticated, power-efficient classification are finding their way into thin-film electronics. Thin-film technologies require robust, layout-efficient devices with facile manufacturability. Here, we show how the multimodal transistor’s (MMT’s) transfer characteristic, with linear dependence in saturation, replicates the rectified linear unit (ReLU) activation function of convolutional ANNs (CNNs). Using MATLAB, we evaluate CNN performance using systematically distorted ReLU functions, then substitute measured and simulated MMT transfer characteristics as proxies for ReLU. High classification accuracy is maintained, despite large variations in geometrical and electrical parameters, as CNNs use the same activation functions for training and classification.