Memristors are promising building blocks for the next-generation memory and neuromorphic computing systems. Most memristors use materials that are incompatible with the silicon dominant complementary metal-oxide-semiconductor technology, and require external selectors in order for large memristor arrays to function properly. Here we demonstrate a fully foundry-compatible, all-silicon-based and self-rectifying memristor that negates the need for external selectors in large arrays. With a p-Si/SiO₂/n-Si structure, our memristor exhibits repeatable unipolar resistance switching behaviour (10⁵ rectifying ratio, 10⁴ ON/OFF) and excellent retention at 300 °C. We further build three-dimensinal crossbar arrays (up to five layers of 100 nm memristors) using fluid-supported silicon membranes, and experimentally confirm the successful suppression of both intra- and inter-layer sneak path currents through the built-in diodes. The current work opens up opportunities for low-cost mass production of three-dimensional memristor arrays on large silicon and flexible substrates without increasing circuit complexity.

Memristive devices are promising candidates for the next generation non-volatile memory and neuromorphic computing. It has been widely accepted that the motion of oxygen anions leads to the resistance changes for valence-change-memory (VCM) type of materials. Only very recently it was speculated that metal cations could also play an important role, but no direct physical characterizations have been reported yet. Here we report a Ta/HfO2/Pt memristor with fast switching speed, record high endurance (120 billion cycles) and reliable retention. We programmed the device to 24 discrete resistance levels, and also demonstrated over a million (2(20)) epochs of potentiation and depression, suggesting that our devices can be used for both multi-level non-volatile memory and neuromorphic computing applications. More importantly, we directly observed a sub-10 nm Ta-rich and O-deficient conduction channel within the HfO2 layer that is responsible for the switching. This work deepens our understanding of the resistance switching mechanism behind oxide-based memristive devices and paves the way for further device performance optimization for a broad spectrum of applications.

Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbO_x Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.