Physical neural networks made of analog resistive switching processors are promising platforms for analog computing. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Herein, we demonstrate the behavior of an alternative synapse design that relies on a deterministic charge-controlled mechanism, modulated electrochemically in solid-state. The device operates by shuffling the smallest cation, the proton, in a threeterminal configuration. It has a channel of active material, WO 3. A solid proton reservoir layer, PdH x , also serves as the gate terminal. A proton conducting solid electrolyte separates the channel and the reservoir. By protonation/deprotonation, we modulate the electronic conductivity of the channel over seven orders of magnitude, obtaining a continuum of resistance states. Proton intercalation increases the electronic conductivity of WO 3 by increasing both the carrier density and mobility. This switching mechanism offers low energy dissipation, good reversibility, and high symmetry in programming.

We combine ultrafast electron diffraction and time-resolved terahertz spectroscopy measurements to unravel the connection between structure and electronic transport properties during the photoinduced insulator-metal transitions in vanadium dioxide. We determine the structure of the metastable monoclinic metal phase, which exhibits anti-ferroelectric charge order arising from a thermally activated, orbital-selective phase transition in the electron system. The relative contribution of this photoinduced monoclinic metal (which has no equilibrium analog) and the photoinduced rutile metal (known from the equilibrium phase diagram) to the time and pump-fluence dependent multi-phase character of the film is established, as is the respective impact of these two distinct phase transitions on the observed changes in terahertz conductivity. Our results represent an important new example of how light can control the properties of strongly correlated materials and elucidate that multimodal experiements are essential when seeking a detailed connection between ultrafast changes in optical-electronic properties and lattice structure in complex materials.The insulator-metal transition (IMT) in vanadium dioxide (VO 2 ) is a benchmark problem in condensed matter physics 1-6 , as it provides a rich playground on which lattice-structural distortions and strong electron correlations conspire to determine emergent material properties. The equilibrium phase diagram of pure VO 2 involves a high-temperature tetragonal (rutile, R) metal that is separated from several structurally distinct lowtemperature insulating phases (monoclinic M 1 , M 2 and triclinic T ) depending on pressure or lattice strain. The transition to these lower-symmetry insulating phases occurs in the vicinity of room temperature and is sensitive to doping (Cr and W), making VO 2 interesting for a range of technological applications 7-9 . Since its discovery there has been a lively discussion in the literature about the driving force responsible for the IMT in VO 2 and the nature of the insulating and metallic phases that has revolved around the role and relative importance of electron-lattice and electron-electron interactions. The stark dichotomy between Peierls 2 and Mott 10 pictures characterizing the earliest explanations have recently given way to a nuanced view that the insulating phases of VO 2 are non-standard Mott-Hubbard systems where both electron-lattice and electron-electron interactions play important roles in determining the electronic properties of all the equilibrium phases 11-16 . Photoexcitation using ultrafast laser pulses has provided another route to initiate the transition between the insulating and metallic phases of VO 2 since it was discovered that the IMT occurs very rapidly following femtosecond laser excitation with sufficient fluence 17 . Since this discovery, VO 2 has been the focus of many time-resolved experiments including X-ray 18,19 and electron 20-22 diffraction, X-ray absorption 23,24 , photoemission 25 and optical spectroscopies 2...

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Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller than biological cells, but it is not yet clear how much faster they can be relative to neurons and synapses. Scaling analyses of ionic transport and charge-transfer reaction rates point to operation in the nonlinear regime, where extreme electric fields are present within the solid electrolyte and its interfaces. In this work, we generated silicon-compatible nanoscale protonic programmable resistors with highly desirable characteristics under extreme electric fields. This operation regime enabled controlled shuttling and intercalation of protons in nanoseconds at room temperature in an energy-efficient manner. The devices showed symmetric, linear, and reversible modulation characteristics with many conductance states covering a 20× dynamic range. Thus, the space-time-energy performance of the all–solid-state artificial synapses can greatly exceed that of their biological counterparts.