Publications

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Automated vehicles (AV) hold great promise for improving safety, as well as reducing congestion and emissions. In order to make automated vehicles commercially viable, a reliable and highperformance vehicle-based computing platform that meets ever-increasing computational demands will be key. Given the state of existing digital computing technology, designers will face significant challenges in meeting the needs of highly automated vehicles without exceeding thermal constraints or consuming a large portion of the energy available on vehicles, thus reducing range between charges or refills. The accompanying increases in energy for AV use will place increased demand on energy production and distribution infrastructure, which also motivates increasing computational energy efficiency.

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Understanding the impact of high-energy electron radiation on device characteristics remains critical for the expanding use of semiconductor electronics in space-borne applications and other radiation harsh environments. Here, we report on in situ measurements of high-energy electron radiation effects on the hole diffusion length in low threading dislocation density homoepitaxial bulk n-GaN Schottky diodes using electron beam induced current (EBIC) in high-voltage scanning electron microscopy mode. Despite the large interaction volume in this system, quantitative EBIC imaging is possible due to the sustained collimation of the incident electron beam. This approach enables direct measurement of electron radiation effects without having to thin the specimen. Using a combination of experimental EBIC measurements and Monte Carlo simulations of electron trajectories, we determine a hole diffusion length of 264 ± 11 nm for n-GaN. Irradiation with 200 kV electron beam with an accumulated dose of 24 × 1016 electrons cm−2 led to an approximate 35% decrease in the minority carrier diffusion length.

Advanced GaN power devices are promising for many applications in high power electronics but performance limitations due to material quality in etched-and-regrown junctions prevent their widespread use. Carrier diffusion length is a critical parameter that not only determines device performance but is also a diagnostic of material quality. Here we present the use of electron-beam induced current to measure carrier diffusion lengths in continuously grown and etched-and-regrown GaN pin diodes as models for interfaces in more complex devices. Variations in the quality of the etched-and-regrown junctions are observed and shown to be due to the degradation of the n-type material. We observe an etched-and-regrown junction with properties comparable to a continuously grown junction.

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The rapidly increasing use of electronics in high-radiation environments and the continued evolution in transistor architectures and materials demand improved methods to characterize the potential damaging effects of radiation on device performance. Here, electron-beam-induced current is used to map hot-carrier transport in model metal-oxide semiconductor field-effect transistors irradiated with a 300 KeV focused He+ beam as a localized line spanning across the gate and bulk Si. By correlating the damage to the electronic properties and combining these results with simulations, the contribution of spatially localized radiation damage on the device characteristics is obtained. This identified damage, caused by the He+ beam, is attributed to localized interfacial Pb centers and delocalized positive fixed-charges, as surmised from simulations. Comprehension of the long-term interaction and mobility of radiation-induced damage are key for future design of rad-hard devices.

Liquid organic hydrogen carriers such as alcohols and polyols are a high-capacity means of transporting and reversibly storing hydrogen that demands effective catalysts to drive the (de)hydrogenation reactions under mild conditions. We employed a combined theory/experiment approach to develop MOF-74 catalysts for alcohol dehydrogenation and examine the performance of the open metal sites (OMS), which have properties analogous to the active sites in high-performance single-site catalysts and homogeneous catalysts. Methanol dehydrogenation was used as a model reaction system for assessing the performance of five monometallic M-MOF-74 variants (M = Co, Cu, Mg, Mn, Ni). Co-MOF-74 and Ni-MOF-74 give the highest H2 productivity. However, Ni-MOF-74 is unstable under reaction conditions and forms metallic nickel particles. To improve catalyst activity and stability, bimetallic (NixMg1-x)-MOF-74 catalysts were developed that stabilize the Ni OMS and promote the dehydrogenation reaction. An optimal composition exists at (Ni0.32Mg0.68)-MOF-74 that gives the greatest H2 productivity, up to 203 mL gcat-1 min-1 at 300 °C, and maintains 100% selectivity to CO and H2 between 225-275 °C. The optimized catalyst is also active for the dehydrogenation of other alcohols. DFT calculations reveal that synergistic interactions between the open metal site and the organic linker lead to lower reaction barriers in the MOF catalysts compared to the open metal site alone. This work expands the suite of hydrogen-related reactions catalyzed by MOF-74 which includes recent work on hydroformulation and our earlier reports of aryl-ether hydrogenolysis. Moreover, it highlights the use of bimetallic frameworks as an effective strategy for stabilizing a high density of catalytically active open metal sites. This journal is

We evaluate the sensitivity of neuromorphic inference accelerators based on silicon-oxide-nitride-oxide-silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40-nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack and predict the effect of TID on intermediate device states between 'program' and 'erase.' This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks - CIFAR-10 and the challenging ImageNet data set - using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the data sets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

The III-nitride semiconductors have many attractive properties for field-emission vacuum electronics, including high thermal and chemical stability, low electron affinity, and high breakdown fields. Here, we report top-down fabricated gallium nitride (GaN)-based nanoscale vacuum electron diodes operable in air, with record ultralow turn-on voltages down to ∼0.24 V and stable high field-emission currents, tested up to several microamps for single-emitter devices. We leverage a scalable, top-down GaN nanofabrication method leading to damage-free and smooth surfaces. Gap-dependent and pressure-dependent studies provide new insights into the design of future, integrated nanogap vacuum electron devices. The results show promise for a new class of high-performance and robust, on-chip, III-nitride-based vacuum nanoelectronics operable in air or reduced vacuum.

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Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

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It is well established that the miniaturization of batteries has not kept pace with the miniaturization of electronics. Three-dimensional (3D) batteries, which were developed with the intent of improving microbattery performance, have had limited success because of fabrication challenges and material constraints. Solid-state, 3D batteries have been particularly susceptible to these shortcomings. In this paper, we demonstrate that the incorporation of a high-conductivity, solid electrolyte is the key to achieving a nonplanar solid-state battery with high areal capacity and high power density. The model 2.5D platform used in this study is a modification of the more typical 3D configuration in that it is comprised of a cathode array of pillars (3D) and a planar (two-dimensional, 2D) anode. This 2.5D geometry exploits the use of a high-conductivity, ionogel electrolyte (10-3 S cm-1), which interpenetrates the 3D electrode array. The 2.5D battery offers high areal energy densities from the post array, while the high-conductivity, solid electrolyte enables high power densities (3.7 mWh cm-2 at 2.8 mW cm-2). The reported solid-state 2.5D device exceeds the energy and power densities of any 3D solid-state system and the derived multiphysics model provides guidance for achieving significantly higher energy and power densities.

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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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Materials exhibiting metal-to-insulator transitions (MITs) could enable low power neuromorphic computing, but progress is hindered by insufficient mechanistic understanding. In this issue of Matter, Banerjee and colleagues describe with intricate detail a new MIT mechanism in β′-CuxV2O5, with potential applications to neuromorphic computing. Materials exhibiting metal-to-insulator transitions (MITs) could enable low power neuromorphic computing, but progress is hindered by insufficient mechanistic understanding. In this issue of Matter, Banerjee and colleagues describe with intricate detail a new MIT mechanism in β′-CuxV2O5, with potential applications to neuromorphic computing.