Training a Neural Network on Analog TaO_x ReRAM Devices Irradiated With Heavy Ions: Effects on Classification Accuracy Demonstrated With CrossSim

The image classification accuracy of a TaO_x ReRAM-based neuromorphic computing accelerator is evaluated after intentionally inducing a displacement damage up to a fluence of 10¹⁴ 2.5-MeV Si ions/cm² on the analog devices that are used to store weights. Results are consistent with a radiation-induced oxygen vacancy production mechanism. When the device is in the high-resistance state during heavy ion radiation, the device resistance, linearity, and accuracy after training are only affected by high fluence levels. The findings in this paper are in accordance with the results of previous studies on TaO_x-based digital resistive random access memory. When the device is in the low-resistance state during irradiation, no resistance change was detected, but devices with a 4-kΩ inline resistor did show a reduction in accuracy after training at 10¹⁴ 2.5-MeV Si ions/cm². This indicates that changes in resistance can only be somewhat correlated with changes to devices' analog properties. This paper demonstrates that TaO_x devices are radiation tolerant not only for high radiation environment digital memory applications but also when operated in an analog mode suitable for neuromorphic computation and training on new data sets.