Publications

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This paper evaluates the effects of ionizing radiation on tantalum oxide (TaOx) memristors. The data obtained from 60Co gamma ray and 10 keV X-ray ionizing radiation experiments indicate that it is possible for the devices to switch from a high resistance off-state to a low resistance on-state after a total ionizing dose (TID) step stress threshold has been surpassed. During irradiation, the devices were floating, grounded, or biased with a 1 Hz square wave with an amplitude of ±100 mV. While floating the terminals is not a typical bias condition within a circuit, it is speculated that this condition might be worst-case because of the lack of a discharge path. If a read measurement is performed prior to reaching the charge threshold, the devices 'reset' back to a pre-irradiation state. This suggests that the devices do not have a cumulative TID effect. However, it was observed that having a continuous bias on the device during the TID exposure did not always have the same effect. The TID threshold level at which the devices switch resistance states varies from device to device; the enhanced susceptibility observed in some devices is still under investigation. After a radiation-induced resistance change, all of the devices could be reset and still functioned properly. When the devices were set into a low resistance on-state prior to irradiation, there was not a significant variation in the resistance post-irradiation (i.e., the devices were still in the on-state). Overall, the memristor TID performance is promising and could potentially enable the discovery of a radiation-hardened nonvolatile memory technology to be used in space and aerospace applications. © 2014 IEEE.

Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.

Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.

A method for extracting interface trap density (DIT) from subthreshold I-V characteristics is used to analyze data on a SiC MOSFET stressed for thirty minutes at 175°C with a gate bias of-20 V. Without knowing the channel doping, the change in DIT can be calculated when referenced to an energy level correlated with the threshold voltage. © 2014 IEEE.

Resistive memory technologies, in particular redox random access memory (ReRAM), are poised as one of the most prominent emerging memory categories to replace NAND flash and fill the important need for a Storage Class Memory (SCM). This is due to low switching energy, low current switching, high speed, outstanding endurance, scalability below 10 nm, and excellent back-end-of-line CMOS compatibility. Furthermore, the analog aspects of memristors have opened the door for many novel applications such as analog math accelerators and neuromorphic computers. This paper provides an overview of resistive memory technologies and their current status, with a focus on redox RAM (ReRAM). © 2014 IEEE.

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The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we will instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.

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