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AlGaN High Electron Mobility Transistor for Power Switches and High Temperature Logic

Klein, Brianna A.; Armstrong, Andrew A.; Allerman, A.A.; Nordquist, Christopher N.; Neely, Jason C.; Reza, Shahed R.; Douglas, Erica A.; Van Heukelom, Michael V.; Rice, Anthony R.; Patel, Victor J.; Matins, Benjamin M.; Fortune, Torben R.; Rosprim, Mary R.; Caravello, Lisa N.; DeBerry, Rebecca N.; Pipkin, Jennifer R.; Abate, Vincent M.; Kaplar, Robert K.

Abstract not provided.

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TYPE Conference Presenation YEAR 2021
OSTIDOI

Demonstration of a 9 kV reverse breakdown and 59 mΩ-cm2 specific on-resistance AlGaN/GaN Schottky barrier diode

Solid-State Electronics

Colón, Albert; Douglas, Erica A.; Pope, Andrew J.; Klein, Brianna A.; Stephenson, Chad A.; Van Heukelom, Michael V.; Tauke-Pedretti, Anna; Baca, A.G.

Al0.26Ga0.74N/GaN on SiC lateral Schottky diodes were fabricated with variable anode-to-cathode spacing and were analyzed for blocking and on-state device performance. On-chip normally-on High Electron Mobility Transistor (HEMT) structures were also fabricated for a comparison of blocking characteristics. The Schottky diode displayed an ideality factor of 1.59 with a Ni/AlGaN zero bias barrier height of 1.18 eV and a flat band barrier height of 1.59 eV. For anode-to-cathode spacings between 10 and 100 μm, an increase in median breakdown voltages from 529 V to 8519 V and median specific on-resistance (Ron-sp) from 1.5 to 60.7 mΩ cm2 was observed with an increase in spacing. The highest performing diode had a lateral figure of merit of 1.37 GW/cm2 corresponding to a breakdown voltage upwards of 9 kV and a Ron-sp of 59 mΩ cm2. This corresponds to the highest Schottky diode breakdown voltage reported thus far with an Al0.26Ga0.74N/GaN lateral structure.

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TYPE Journal Article YEAR 2019
ScopusOSTIDOI

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

Conference Proceedings - IEEE International Conference on Rebooting Computing (ICRC)

Jacobs-Gedrim, Robin B.; Agarwal, Sapan A.; Knisely, Kathrine E.; Stevens, Jim E.; Van Heukelom, Michael V.; Hughart, David R.; James, Conrad D.; Marinella, Matthew J.

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaOx, and two conducting metallization systems, Cu-SiO2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.

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TYPE Journal Article YEAR 2017
OSTIDOI

Impact of linearity and write noise of analog resistive memory devices in a neural algorithm accelerator

2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings

Jacobs-Gedrim, Robin B.; Agarwal, Sapan A.; Knisely, Kathrine E.; Stevens, Jim E.; Van Heukelom, Michael V.; Hughart, David R.; Niroula, John; James, Conrad D.; Marinella, Matthew J.

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaOx, and two conducting metallization systems, Cu-SiO2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. This suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.

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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI
Results 1–25 of 31
Results 1–25 of 31