Here we report on AlGaN high electron mobility transistor (HEMT)-based logic development, using combined enhancement- and depletion-mode transistors to fabricate inverters with operation from room temperature up to 500°C. Our development approach included: (a) characterizing temperature-dependent carrier transport for different AlGaN HEMT heterostructures, (b) developing a suitable gate metal scheme for use in high temperatures, and (c) over-temperature testing of discrete devices and inverters. Hall mobility data (from 30°C to 500°C) revealed the reference GaN-channel HEMT experienced a 6.9x reduction in mobility, whereas the AlGaN channel HEMTs experienced about a 3.1x reduction. Furthermore, a greater aluminum contrast between the barrier and channel enabled higher carrier densities in the two-dimensional electron gas for all temperatures. The combination of reduced variation in mobility with temperature and high sheet carrier concentration showed that an Al-rich AlGaN-channel HEMT with a high barrier-to-channel aluminum contrast is the best option for an extreme temperature HEMT design. Three gate metal stacks were selected for low resistivity, high melting point, low thermal expansion coefficient, and high expected barrier height. The impact of thermal cycling was examined through electrical characterization of samples measured before and after rapid thermal anneal. The 200-nm tungsten gate metallization was the top performer with minimal reduction in drain current, a slightly positive threshold voltage shift, and about an order of magnitude advantage over the other gates in on-to-off current ratio. After incorporating the tungsten gate metal stack in device fabrication, characterization of transistors and inverters from room temperature up to 500°C was performed. The enhancement-mode (e-mode) devices’ resistance started increasing at about 200°C, resulting in drain current degradation. This phenomenon was not observed in depletion-mode (d-mode) devices but highlights a challenge for inverters in an e-mode driver and d-mode load configuration.
We report on AlGaN HEMT-based logic development, using combined enhancement- and depletion-mode transistors to fabricate inverters with operation from room temperature up to 500°C. Our development approach included: (a) characterizing temperature dependent carrier transport for different AlGaN HEMT heterostructures, (b) developing a suitable gate metal scheme for use in high temperatures, and (c) over-temperature testing of discrete devices and inverters. Hall mobility data revealed the GaN-channel HEMT experienced a 6.9× reduction in mobility, whereas the AlGaN channel HEMTs experienced about a 3.1x reduction. Furthermore, a greater aluminum contrast between the barrier and channel enabled higher carrier densities in the two-dimensional electron gas for all temperatures. The combination of reduced variation in mobility with temperature and high sheet carrier concentration showed that an Al-rich AlGaN-channel HEMT with a high barrier-to-channel aluminum contrast is the best option for an extreme temperature HEMT design. Three gate metal stacks were selected for low resistivity, high melting point, low thermal expansion coefficient, and high expected barrier height. The impact of thermal cycling was examined through electrical characterization of samples measured before and after rapid thermal anneal. The 200 nm tungsten gate metallization was the top performer with minimal reduction in drain current, a slightly positive threshold voltage shift, and about an order of magnitude advantage over the other gates in on-to-off current ratio. After incorporating the tungsten gate metal stack in device fabrication, characterization of transistors and inverters from room temperature up to 500°C was performed. The enhancement-mode (e-mode) devices’ resistance started increasing at about 200°C, resulting in drain current degradation. This phenomenon was not observed in depletion-mode (d-mode) devices but highlights a challenge for inverters in an e-mode driver and d-mode load configuration.
Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) can be spread through close contact or through fomite mediated transmission. This study details the fabrication and analysis of a photocatalyst surface which can rapidly inactivate SARS-COV-2 to limit spread of the virus by fomite mediated transmission. The surface being developed at Sandia for this purpose is a minimally hazardous Ag-Ti0 2 nanomaterial which is engineered to have high photocatalytic activity. Initial results at Sandia California in a BSL-2 safe surrogate virus- Vesicular Stomatitis Virus (VSV) show a significant difference between the photocatalyst material under exposure to visible light than controls. Additionally, UV-A light (365 nm) was found to eliminate SARS-COV-2 after 9 hours on all tested surfaces with irradiance of 15 mW/cm 2 equivalent to direct circumsolar irradiance.
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.
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.
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.