Sapan Agarwal

Defect creation mechanisms in amorphous (a) Ta2O5 films employed in various microelectronics applications, e.g., resistive switching memories, are still poorly understood. To address this issue, we investigate the role of electron and hole injection in defect creation in a-Ta2O5. Our Density Functional Theory calculations demonstrate that electrons and holes can be trapped in polaron and bipolaron states at structural precursor sites in a-Ta2O5. The bipolaron creation significantly reduces the average energies for the formation of Frenkel pairs (FPs) of oxygen vacancies and interstitial oxygen ions to about 1.4 eV for hole trapping and 3.2 eV for electron trapping. The average diffusion barriers of the O interstitials are 0.3 eV for O2− and 1.4 eV for O0, which forms a bond with a neighboring O ion at the site. Our results predict that hole bipolaron formation provides the lowest energy pathways for FP creation in stoichiometric a-Ta2O5. They can be used in further theoretical simulations of the mechanisms of degradation processes in a-Ta2O5 and to guide experimental studies.

Analog in-memory computing (AIMC) utilizing nonvolatile resistive memory arrays can drastically reduce the energy required for neural network inference. The oxide-based resistive random access memory (ReRAM) devices are a promising candidate for AIMC due to their high density and ability to store multiple bits of precision in an analog state. However, variability in conductance fluctuations across devices and programming cycles in each device presents a challenge for inference accuracy. We characterize the signature of conductance fluctuations in a Ta/Ta₂O_5-x ReRAM array, finding significant variability in fluctuations from cycle-to-cycle associated with different filament configurations, which ultimately causes high conductance variability at the conclusion of the settling period of $\approx 1$ h following programming. Although cycle-to-cycle variability and device-to-device fluctuation variability were similar, specific devices tended toward high cycle-to-cycle variability. Most devices had an optimal filamentary configuration, which was relatively stable over time. By creating a smart write-verify routine, which determines the optimal filament configuration, it was possible to improve the inference accuracy for CIFAR-10 image recognition from 28.5% to 79.1% as measured following the 1-h settling period.

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. In this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these analog in-memory compute systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2D Fourier Transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. Additionally, the impact of errors introduced by the analog hardware are explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Electrochemical random-access memory (ECRAM) is an emerging three-terminal nonvolatile memory with highly controllable channel conductance, and hence is promising for use as an analog memory (or synapse) in analog in-memory computing (AIMC) systems. Energy efficient AIMC computing is particularly desirable for power-constrained, high radiation environments such as satellites. However, little is known about the suitability of ECRAM for use in a total ionizing dose (TID) environment. This work investigates the effect of Co-60 gamma radiation on the channel conductance and noise - two properties critical for AIMC systems - of a TaO_x-based ECRAM up to 17.3 Mrad(SiO₂) for both low and high channel conductance state devices. A transient increase in conductance is observed in response to radiation which consists of two elements: an immediate increase photoconductivity and a secondary photocurrent, which has a slower rise and saturation and can persist for hours after exposure and is attributed to charging due to hole trapping. These transient effects would not likely occur in a space environment due to the low dose rate. No permanent change is found in the low conductance state following exposure and the minor shift in the high conductance change would be less significant than the regular retention decay in this state. A permanent increase in the random telegraph noise is observed, possibly due to increased traps created in the channel. This work demonstrates that TaO_xbased ECRAM is suitable for use in spaceborne AIMC systems that are subject to significant TID.

Silicon-oxide-nitride-oxide-silicon (SONOS) charge-trap memory is a promising nonvolatile memory device for analog in-memory computing applications. The sensitivity of 40nm SONOS analog states in response to single event effects (SEE) were experimentally assessed by exposing a SONOS array to heavy ions with LET’s of 28.6, 44.9, and 59.5 MeV/(mg/cm²). A statistical model of SEE was developed using specific criteria to distinguish them from effects unrelated to radiation, such as device-to-device programming variation, read noise, and drift. This model was used to simulate the effects of heavy ions on an analog in-memory computing (AIMC) accelerator processing standard deep neural networks running inference on common datasets, including ImageNet. Simulated inference accuracy decreases with increasing LET and fluence. At the highest LET of 59.5 MeV-cm²/mg, an accuracy degradation of 5% on ImageNet was predicted at a fluence of 3x10⁶ ions/cm², indicating a sufficient performance for most space applications.