We evaluate the resilience of CoFeB/MgO/CoFeB magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) to displacement damage induced by heavy-ion irradiation. MTJs were exposed to 3-MeV Ta2+ ions at different levels of ion beam fluence spanning five orders of magnitude. The devices remained insensitive to beam fluences up to $10^{11}$ ions/cm2, beyond which a gradual degradation in the device magnetoresistance, coercive magnetic field, and spin-transfer-torque (STT) switching voltage were observed, ending with a complete loss of magnetoresistance at very high levels of displacement damage (>0.035 displacements per atom). The loss of magnetoresistance is attributed to structural damage at the MgO interfaces, which allows electrons to scatter among the propagating modes within the tunnel barrier and reduces the net spin polarization. Ion-induced damage to the interface also reduces the PMA. This study clarifies the displacement damage thresholds that lead to significant irreversible changes in the characteristics of STT magnetic random access memory (STT-MRAM) and elucidates the physical mechanisms underlying the deterioration in device properties.
Scaling arrays of non-volatile memory devices from academic demonstrations to reliable, manufacturable systems requires a better understanding of variability at array and wafer-scale levels. CrossSim models the accuracy of neural networks implemented on an analog resistive memory accelerator using the cycle-to-cycle variability of a single device. In this work, we extend this modeling tool to account for device-to-device variation in a realistic way, and evaluate the impact of this reliability issue in the context of neuromorphic online learning tasks.
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.
With the growing interest to explore Jupiter's moons, technologies with +10 Mrad(Si) tolerance are now needed, to survive the Jovian environment. Conductive-bridging random access memory (CBRAM) is a nonvolatile memory that has shown a high tolerance to total ionizing dose (TID). However, it is not well understood how CBRAM behaves in an energetic ion environment where displacement damage (DD) effects may also be an issue. In this paper, the response of CBRAM to 100-keV Li, 1-MeV Ta, and 200-keV Si ion irradiations is examined. Ion bombardment was performed with increasing fluence steps until the CBRAM devices failed to hold their programed state. The TID and DD dose (DDD) at the fluence of failure were calculated and compared against tested ion species. Results indicate that failures are more highly correlated with TID than DDD. DC cycling tests were performed during 100-keV Li irradiations and evidence was found that the mobile Ag ion supply diminished with increasing fluence. The cycling results, in addition to prior 14-MeV neutron work, suggest that DD may play a role in the eventual failure of a CBRAM device in a combined radiation environment.
The image classification accuracy of a TaOx 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 TaOx-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 TaOx 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.