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 SiO x (∼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 SiO x 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.

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Electronic synapses based on nonperovskite CsPbI₃ demonstrate a variety of synaptic characteristics with superior ambient stability.

Although experimental implementations of memristive crossbar arrays have indicated the potential of these networks for in-memory computing, their performance is generally limited by an intrinsic variability on the device level as a result of the stochastic formation of conducting filaments. A tunnel-type memristive device typically exhibits small switching variations, owing to the relatively uniform interface effect. However, the low mobility of oxygen ions and large depolarization field result in slow operation speed and poor retention. Here, we demonstrate a quantum-tunneling memory with Ag-doped percolating systems, which possesses desired characteristics for large-scale artificial neural networks. The percolating layer suppresses the random formation of conductive filaments, and the nonvolatile modulation of the Fowler−Nordheim tunneling current is enabled by the collective movement of active Ag nanocrystals with high mobility and a minimal depolarization field. Such devices simultaneously possess electroforming-free characteristics, record low switching variabilities (temporal and spatial variation down to 1.6 and 2.1%, respectively), nanosecond operation speed, and long data retention (>10 4 s at 85 °C). Simulations prove that passive arrays with our analog memory of large current−voltage nonlinearity achieve a high write and recognition accuracy. Thus, our discovery of the unique tunnel memory contributes to an important step toward realizing neuromorphic circuits.

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