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.
We evaluate the sensitivity of neuromorphic inference accelerators based on silicon-oxide-nitride-oxide-silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40-nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack and predict the effect of TID on intermediate device states between 'program' and 'erase.' This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks - CIFAR-10 and the challenging ImageNet data set - using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the data sets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.
We report electrically detected magnetic resonance (EDMR) results in metal-oxidesemiconductor field effect transistors before and after high field gate stressing. The measurements utilize EDMR detected through interface recombination currents. These interface recombination measurements provide information about one aspect of the stressing damage: The chemical and physical identity of trapping centers generated at and very near the interface. EDMR signal demonstrates that interface defects known as centers play important roles in the stress-induced damage.
It is widely accepted that the breakdown of Si02 gate dielectrics is caused by the buildup of stress-induced defects over time. Although several physical mechanisms have been proposed for the generation of these defects, very little direct experimental evidence as to the chemical and physical identity of these defects has been generated in the literature thus far. Here, we present electrically detected magnetic resonance (EDMR) measurements obtained via spin-dependent recombination currents at the interface of high-field stressed Si/Si02 metal-oxide-semiconductor field effect transistors (MOSFETs).
The radiation response of TaOx-based RRAM devices fabricated in academic (Set A) and industrial (Set B) settings was compared. Ionization damage from a 60Co gamma source did not cause any changes in device resistance for either device type, up to 45 Mrad(Si). Displacement damage from a heavy ion beam caused the Set B in the high resistance state to decrease in resistance at 1 x 1021 oxygen displacements per cm3; meanwhile, the Set A devices did not exhibit any decrease in resistance due to displacement damage. Both types of devices exhibited an increase in resistance around 3 x 1022 oxygen displacements per cm3, possibly due to damage at the oxide/metal interfaces. These extremely high levels of damage represent near-total atomic disruption, and if this level of damage were ever reached, other circuit elements would likely fail before the RRAM devices in this study. Overall, both sets of devices were much more resistant to radiation effects than other devices reported in the literature. Displacement damage effects were only observed in the Set A devices once the displacement-induced oxygen vacancies surpassed the intrinsic vacancy concentration in the devices, suggesting that high oxygen vacancy concentration played a role in the devices’ high tolerance to displacement damage.
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.
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. Here, 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.
