Machine learning

Physics

Conductance

Engineering

Yield (engineering)

Physical chemistry

Memristor

Neuromorphic engineering

Composite material

Nanotechnology

Electrode

Organic chemistry

Resistive random-access memory

Chemistry

Memistor

Materials science

Electronic engineering

Condensed matter physics

Silicon nitride

Silicon oxide

Computer science

Optoelectronics

Artificial neural network

Silicon

Amorphous solid

ACS Applied Materials & Interfaces

Memristors based on two-dimensional (2D) materials can exhibit great scalability and ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin electrolytes lowers the production yield and reliability of devices. Here, we report that the self-limiting amorphous SiOx (∼2.7 nm) provides a perfect atomically thin electrolyte with high uniformity, featuring a record high production yield. With the guidance of physical modeling, we reveal that the atomic thickness of SiOx enables anomalous resistive switching with a transition to an analog quasi-reset mode, where the filament stability can be further enhanced using Ag-Au nanocomposite electrodes. Such a picojoule memristor shows record low switching variabilities (C2C and D2D variation down to 1.1 and 2.6%, respectively), good retention at a few microsiemens, and high conductance-updating linearity, constituting key metrics for analog neural networks. In addition, the stable high-resistance state is found to be an excellent source for true random numbers of Gaussian distribution. This work opens up opportunities in mass production of Si-compatible memristors for ultradense neuromorphic and security hardware.

Reliable Memristor Based on Ultrathin Native Silicon Oxide