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Abstract State estimation in multi-layer turbulent flow fields with only a single layer of partial observation remains a challenging yet practically important task. Applications include inferring the state of the deep ocean by exploiting surface observations. Directly implementing an ensemble Kalman filter based on the full forecast model is usually expensive. One widely used method in practice projects the information of the observed layer to other layers via linear regression. However, large errors appear when nonlinearity in the highly turbulent flow field becomes dominant. In this paper, we develop a multi-step nonlinear data assimilation method that involves the sequential application of nonlinear assimilation steps across layers. Unlike traditional linear regression approaches, a conditional Gaussian nonlinear system is adopted as the approximate forecast model to characterize the nonlinear dependence between adjacent layers. At each step, samples drawn from the posterior of the current layer are treated as pseudo-observations for the next layer. Each sample is assimilated using analytic formulae for the posterior mean and covariance. The resulting Gaussian posteriors are then aggregated into a Gaussian mixture. Therefore, the method can capture strongly turbulent features, particularly intermittency and extreme events, and more accurately quantify the inherent uncertainty. Applications to the two-layer quasi-geostrophic system with Lagrangian data assimilation demonstrate that the multi-step method outperforms the one-step method, particularly as the tracer number and ensemble size increase. Results also show that the multi-step CGDA is particularly effective for assimilating frequent, high-accuracy observations, which are scenarios where traditional EnKF methods may suffer from catastrophic filter divergence. 

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies. 

Abstract The increasing interests in analog computing nowadays call for multipurpose analog computing platforms with reconfigurability. The advancement of analog computing, enabled by novel electronic elements like memristors, has shown its potential to sustain the exponential growth of computing demand in the new era of analog data deluge. Here, a platform of a memristive field‐programmable analog array (memFPAA) is experimentally demonstrated with memristive devices serving as a variety of core analog elements and CMOS components as peripheral circuits. The memFPAA is reconfigured to implement a first‐order band pass filter, an audio equalizer, and an acoustic mixed frequency classifier, as application examples. The memFPAA, featured with programmable analog memristors, memristive routing networks, and memristive vector‐matrix multipliers, opens opportunities for fast prototyping analog designs as well as efficient analog applications in signal processing and neuromorphic computing. 

Abstract The application of hardware‐based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. This work reports direct optical control of an oscillatory neuron based on volatile threshold switching in V₃O₅. The devices exhibit electroforming‐free operation with switching parameters that can be tuned by optical illumination. Using temperature‐dependent electrical measurements, conductive atomic force microscopy (C‐AFM), in situ thermal imaging, and lumped element modelling, it is shown that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photoconductive and bolometric characteristics of V₃O₅, which eventually affects the oscillation dynamics. Furthermore, V₃O₅is identified as a new bolometric material with a temperature coefficient of resistance (TCR) as high as −4.6% K⁻¹at 423 K. The utility of these devices is illustrated by demonstrating in‐sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network (SNN), respectively, using a simulated array of devices. 

Abstract Different from nonvolatile memory applications, neuromorphic computing applications utilize not only the static conductance states but also the switching dynamics for computing, which calls for compact dynamical models of memristive devices. In this work, a generalized model to simulate diffusive and drift memristors with the same set of equations is presented, which have been used to reproduce experimental results faithfully. The diffusive memristor is chosen as the basis for the generalized model because it possesses complex dynamical properties that are difficult to model efficiently. A data set from statistical measurements on SiO₂:Ag diffusive memristors is collected to verify the validity of the general model. As an application example, spike‐timing‐dependent plasticity is demonstrated with an artificial synapse consisting of a diffusive memristor and a drift memristor, both modeled with this comprehensive compact model. 

Sneak path current is a fundamental issue and a major roadblock to the wide application of memristor crossbar arrays. Traditional selectors such as transistors compromise the 2D scalability and 3D stack‐ability of the array, while emerging selectors with highly nonlinear current–voltage relations contradict the requirement of a linear current–voltage relation for efficient multiplication by directly using Ohm's law. Herein, the concept of a timing selector is proposed and demonstrated, which addresses the sneak path issue with a voltage‐dependent delay time of its transient switching behavior, while preserving a linear current–voltage relationship for computation. Crossbar arrays with silver‐based diffusive memristors as the timing selectors are built and the operation principle and operational windows are experimentally demonstrated. The timing selector enables large memristor crossbar arrays that can be used to solve large‐dimension real‐world problems in machine intelligence and neuromorphic computing.