Publications

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The ability to train deep neural networks on large data sets have made significant impacts onto artificial intelligence, but consume significant amounts of energy due to the need to move information from memory to logic units. In-memory "neuromorphic" computing presents an alternative framework that processes information directly on memory elements. In-memory computing has been limited by the poor performance of the analogue information storage element, often phase-change memory or memristors. To solve this problem, we developed two types of "redox transistors" using TiO₂ (anatase) which stores analogue information states through the electrochemical concentration of dopants in the crystal. The first type of redox transistor uses lithium as the electrochemical dopant ion, and its key advantage is low operating voltage. The second uses oxygen vacancies as the dopant, which is CMOS compatible and can retain state even when scaled to nanosized dimensions. Both devices offer significant advantages in terms of predictable analogue switching over conventional filamentary-based devices, and provide a significant advance in developing materials and devices for neuromorphic computing.

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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 Li_XTiO₂ 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 Li_XTiO₂ redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.

Efficiency bottlenecks inherent to conventional computing in executing neural algorithms have spurred the development of novel devices capable of “in-memory” computing. Commonly known as “memristors,” a variety of device concepts including conducting bridge, vacancy filament, phase change, and other types have been proposed as promising elements in artificial neural networks for executing inference and learning algorithms. In this article, we review the recent advances in memristor technology for neuromorphic computing and discuss strategies for addressing the most significant performance challenges, including nonlinearity, high read/write currents, and endurance. As an alternative to two-terminal memristors, we introduce the three-terminal electrochemical memory based on the redox transistor (RT), which uses a gate to tune the redox state of the channel. Decoupling the “read” and “write” operations using a third terminal and storage of information as a charge-compensated redox reaction in the bulk of the transistor enables high-density information storage. These properties enable low-energy operation without compromising analog performance and nonvolatility. Finally, we discuss the RT operating mechanisms using organic and inorganic materials, approaches for array integration, and prospects for achieving the device density and switching speeds necessary to make electrochemical memory competitive with established digital technology.

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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.

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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.

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