Characterization and Stabilization of Short-Term Fluctuations in the Analog Conductance States of Ta/Ta 2 O 5-x ReRAM

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Abstract
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/Ta2O5-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.
Funding Information
  • Sandia’s Laboratory Directed Research and Development (LDRD) program
  • Microelectronics Commons Program, a Department of War Initiative (N00164-23-9-G030)
  • Aerospace Internal Research and Development Funding

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