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The search for high‐performance resistive random‐access memory (RRAM) devices is essential to pave the way for highly efficient non‐Von Neumann computing architecture. Here, it is reported on an alloying approach using atomic layer deposition for a Zn‐doped HfOx‐based resistive random‐access memory (HfZnO RRAM), with improved performance. As compared with HfOx RRAM, the HfZnO RRAM exhibits reduced switching voltages (>20%) and switching energy (>3×), as well as better uniformity both in voltages and resistance states. Furthermore, the HfZnO RRAM exhibits stable retention exceeding 10 years, as well as write/erase endurance exceeding 105 cycles. In addition, excellent linearity and repeatability of conductance tuning can be achieved using the constant voltage pulse scheme, achieving ≈90% accuracy in a simulated multi‐layer perceptron network for the recognition of modified national institute of standards and technology database handwriting. The HfZnO RRAM is also characterized down to the temperature of 4 K, showing functionality and the elucidation of its carrier conduction mechanism. Hence, a potential pathway for doped‐RRAM to be used in a wide range of temperatures including quantum computing and deep‐space exploration is shown.

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Herein, we report a solution-processable memristive device based on bismuth vanadate (BiVO 4 ) and titanium dioxide (TiO 2 ) with gallium-based eutectic gallium−indium (EGaIn) and gallium−indium-tin alloy (GaInSn) liquid metal as the top electrode. Scanning electron microscopy (SEM) shows the formation of a nonporous structure of BiVO 4 and TiO 2 for efficient resistive switching. Additionally, the gallium-based liquid metal (GLM)contacted memristors exhibit stable memristor behavior over a wide temperature range from −10 to +90 °C. Gallium atoms in the liquid metal play an important role in the conductive filament formation as well as the device's operation stability as elucidated by I−V characteristics. The synaptic behavior of the GLM-memristors was characterized, with excellent long-term potentiation (LTP) and longterm depression (LTD) linearity. Using the performance of our device in a multilayer perceptron (MLP) network, a ∼90% accuracy in the handwriting recognition of modified national institute of standards and technology database (MNIST) was achieved. Our findings pave a path for solution-processed/GLM-based memristors which can be used in neuromorphic applications on flexible substrates in a harsh environment.

Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to generate behavioral models of RRAM devices from practical measurement/simulation data. The proposed framework can faithfully capture the evolution of internal state and its impacts on the output. A series of modifications informed by the RRAM device physics are proposed to enhance the modeling capabilities. The integration strategy of Verilog-A equivalent circuits, is also developed for compatibility with existing general-purpose circuit simulators. The Verilog-A model can be easily adopted into the SPICE-type simulator for the circuit design with a variable step that differs from the training process. Numerical experiments with real RRAM devices data demonstrate the feasibility and advantages of the proposed methodology.