No abstract

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in‐sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in‐sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in‐sensor computing is provided.

No abstract

No abstract

No abstract

The fully memristive neural network is emerging as a game‐changer in the artificial intelligence competition. Artificial synapses and neurons, as two fundamental elements for hardware neural networks, have been substantially implemented by different devices with memory and threshold switching (TS) behaviors, respectively. However, obtaining controllable memory and TS behaviors in the same memristive material system is still a considerable challenge that holds great potential for realizing compatible artificial neurons and synapses. Here, a heterogeneous bilayer conductive filamentary memristor comprising two different electrolytes with distinct copper ion mobility is reported: Cu/GeTe/Al2O3/Pt, which can demonstrate the governance of switching types. Experimentally, when the thickness of the Al2O3 layer is 3 nm, stable nonvolatile multilevel memory switching (MS) is observed and employed to mimic the synaptic plasticity. With increasing oxide thickness, the switching behavior under the same compliance current alters from MS to volatile TS and is used to emulate the integrate‐and‐fire neuron function. The controllable switching stems from the change in the metal filament morphology within the Al2O3 layer, which is supported by ab initio calculation results. This method enables a new pathway for constructing functionally reconfigurable neuromorphic devices for intelligence neuromorphic systems.

No abstract