Publications

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The mechanical degradation of all-solid-state Li-ion batteries (ASSLBs) is expected to be more severe than that in traditional Li-ion batteries with liquid electrolytes due to the additional mechanical constraints imposed by the solid electrolyte on the deformation of electrodes. Cracks and fractures could occur both inside the solid electrolyte (SE) and at the SE/electrode interfconce. A coupled electrochemical-mechanical model was developed and solved by the Finite Element Method (FEM) to evaluate the stress development in ASSLBs. Two sources of volume change were considered, namely the expansion/shrinkage of electrodes due to lithium concentration change and the interphase formation at the SE/electrode interface due to the decomposition of SEs. The most plausible solid electrolyte decomposition reactions and their associated volume change were predicted by density functional theory (DFT) calculations. It was found that the stress associated with a volume change due to solid electrolyte decomposition can be much more significant than that of electrode volumetric changes associated with Li insertion/extraction. This model can be used to design 3D ASSLB architectures to minimize their internal stress generation.

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.

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. 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.

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.

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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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With low-cost and simple processing, organic electrochromic polymers have attracted considerable attention as a promising material platform for flexible and low-energy-consuming optoelectronic devices. However, typical electrochromic polymers can only be switched from natural-colored to oxidized-transparent states. As a result, the complexity of combining several distinct polymers to achieve a full-color gamut has significantly limited the niche applications of electrochromic polymers. Here in this paper we report an electrochromic polymer based on 4,7-di((3,3-dimethyl-3,4-dihydro-2H-thieno[3,4-b][1,4]dioxepine-3-yl)-3,4-ethylenedioxythiophene) (PEP), which exhibits fast full-color reversible tuning capability and good stability. Furthermore, a red-green-blue flexible electrochromic device just based on poly(PEP) was fabricated, which offers an effective approach to dynamically manipulate color and enables a variety of optoelectronic applications.

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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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HKUST-1 or Cu3BTC2 (BTC = 1,3,5-benzenetricarboxylate) is a prototypical metal-organic framework (MOF) that holds a privileged position among MOFs for device applications, as it can be deposited as thin films on various substrates and surfaces. Recently, new potential applications in electronics have emerged for this material when HKUST-1 was demonstrated to become electrically conductive upon infiltration with 7,7,8,8-tetracyanoquinodimethane (TCNQ). However, the factors that control the morphology and reactivity of the thin films are unknown. Here, we present a study of the thin-film growth process on indium tin oxide and amorphous Si prior to infiltration. From the unusual bimodal, non-log-normal distribution of crystal domain sizes, we conclude that the nucleation of new layers of Cu3BTC2 is greatly enhanced by surface defects and thus difficult to control. We then show that these films can react with methanolic TCNQ solutions to form dense films of the coordination polymer Cu(TCNQ). This chemical conversion is accompanied by dramatic changes in surface morphology, from a surface dominated by truncated octahedra to randomly oriented thin platelets. The change in morphology suggests that the chemical reaction occurs in the liquid phase and is independent of the starting surface morphology. The chemical transformation is accompanied by 10 orders of magnitude change in electrical conductivity, from <10-11 S/cm for the parent Cu3BTC2 material to 10-1 S/cm for the resulting Cu(TCNQ) film. The conversion of Cu3BTC2 films, which can be grown and patterned on a variety of (nonplanar) substrates, to Cu(TCNQ) opens the door for the facile fabrication of more complex electronic devices.

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Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two-terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three-terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. Here, an all-solid-state electrochemical transistor made with Li ion–based solid dielectric and 2D α-phase molybdenum oxide (α-MoO3) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the α-MoO3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short- and long-term synaptic plasticity and bidirectional near-linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large-scale, energy-efficient neuromorphic computing networks.

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Because of their extraordinary surface areas and tailorable porosity, metal-organic frameworks (MOFs) have the potential to be excellent sensors of gas-phase analytes. MOFs with open metal sites are particularly attractive for detecting Lewis basic atmospheric analytes, such as water. Here, we demonstrate that thin films of the MOF HKUST-1 can be used to quantitatively determine the relative humidity (RH) of air using a colorimetric approach. HKUST-1 thin films are spin-coated onto rigid or flexible substrates and are shown to quantitatively determine the RH within the range of 0.1-5% RH by either visual observation or a straightforward optical reflectivity measurement. At high humidity (>10% RH), a polymer/MOF bilayer is used to slow the transport of H2O to the MOF film, enabling quantitative determination of RH using time as the distinguishing metric. Finally, the sensor is combined with an inexpensive light-emitting diode light source and Si photodiode detector to demonstrate a quantitative humidity detector for low humidity environments.

Detailed understanding of solid–solid interface structure–function relationships is critical for the improvement and wide deployment of all-solid-state batteries. The interfaces between lithium phosphorous oxynitride (LiPON) solid electrolyte material and lithium metal anode, and between LiPON and LixCoO2 cathode, have been reported to generate solid–electrolyte interphase (SEI)-like products and/or disordered regions. Using electronic structure calculations and crystalline LiPON models, we predict that LiPON models with purely P−N−P backbones are kinetically inert towards lithium at room temperature. In contrast, transfer of oxygen atoms from low-energy LixCoO2(104) surfaces to LiPON is much faster under ambient conditions. The mechanisms of the primary reaction steps, LiPON structural motifs that readily reacts with lithium metal, experimental results on amorphous LiPON to partially corroborate these predictions, and possible mitigation strategies to reduce degradations are discussed. LiPON interfaces are found to be useful case studies for highlighting the importance of kinetics-controlled processes during battery assembly at moderate processing temperatures.

Three-dimensional thin-film solid-state batteries (3D TSSB) were proposed by Long et al. in 2004 as a structure-based approach to simultaneously increase energy and power densities. Here, we report experimental realization of fully conformal 3D TSSBs, demonstrating the simultaneous power-and-energy benefits of 3D structuring. All active battery components - electrodes, solid electrolyte, and current collectors - were deposited by atomic layer deposition (ALD) onto standard CMOS processable silicon wafers microfabricated to form arrays of deep pores with aspect ratios up to approximately 10. The cells utilize an electrochemically prelithiated LiV2O5 cathode, a very thin (40-100 nm) Li2PO2N solid electrolyte, and a SnNx anode. The fabrication process occurs entirely at or below 250 °C, promising compatibility with a variety of substrates as well as integrated circuits. The multilayer battery structure enabled all-ALD solid-state cells to deliver 37 μAh/cm2·μm (normalized to cathode thickness) with only 0.02% per-cycle capacity loss. Conformal fabrication of full cells over 3D substrates increased the areal discharge capacity by an order of magnitude while simulteneously improving power performance, a trend consistent with a finite element model. This work shows that the exceptional conformality of ALD, combined with conventional semiconductor fabrication methods, provides an avenue for the successful realization of long-sought 3D TSSBs which provide power performance scaling in regimes inaccessible to planar form factor cells.

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (∼170 fJ • μm-2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.