Plasmonic antennas and metasurfaces can effectively control light-matter interactions, and this facilitates a deterministic design of optical materials properties, including structural color. However, these optical properties are generally fixed after synthesis and fabrication, while many modern-day optics applications require active, low-power, and nonvolatile tuning. These needs have spurred broad research activities aimed at identifying materials and resonant structures capable of achieving large, dynamic changes in optical properties, especially in the challenging visible spectral range. In this work, we demonstrate dynamic tuning of polarization-dependent gap plasmon resonators that contain the electrochromic oxide WO3. Its refractive index in the visible changes continuously from n = 2.1 to 1.9 upon electrochemical lithium insertion and removal in a solid-state device. By incorporating WO3 into a gap plasmon resonator, the resonant wavelength can be shifted continuously and reversibly by up to 58 nm with less than 2 V electrochemical bias voltage. The resonator can remain in a tuned state for tens of minutes under open circuit conditions.
Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into LiXTiO2 that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the LiXTiO2 redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.
Analog crossbars have the potential to reduce the energy and latency required to train a neural network by three orders of magnitude when compared to an optimized digital ASIC. The crossbar simulator, CrossSim, can be used to model device nonidealities and determine what device properties are needed to create an accurate neural network accelerator. Experimentally measured device statistics are used to simulate neural network training accuracy and compare different classes of devices including TaOx ReRAM, Lir-Co-Oz devices, and conventional floating gate SONOS memories. A technique called 'Periodic Carry' can overcomes device nonidealities by using a positional number system while maintaining the benefit of parallel analog matrix operations.
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.
LiXFePO4 (0 < X < 1) is one of the most well-studied cathode battery materials and is notable for its large miscibility gap. Although its phase-separating behaviors under equilibrium conditions have been well documented, recent research has shown that phase separation is suppressed at elevated rates of lithium insertion and removal. Specifically, LiXFePO4 exhibits a nonequilibrium solid solution behavior at elevated cycling rates. This article reviews recent research on nonequilibrium solid solution in LiXFePO4; these insights have been largely enabled by operando characterization techniques. Such studies have not only unambiguously confirmed the existence of this solid solution, but also show how surface reaction and diffusion kinetics ultimately affect phase separation and other spatially nonuniform lithiation and delithiation behavior.