Ionizing radiation effects in a rectifier circuit

Bôas, Alexis C. Vilas; Guazzelli, Marcilei A.; Giacomini, Renato; Medina, N. H.

doi:10.1088/1742-6596/1291/1/012019

Cited by 2 publications

(1 citation statement)

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“…The published work on radiation effects on spiking neural network implementations is extremely limited. There are radiation studies on specific device technologies and more traditional architectures like CMOS structures at multiple technology nodes [48]- [50], MOS oxide [51,52], rectifier circuits [53] and diodes [54].…”

Section: Quantifying Radiationmentioning

confidence: 99%

Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021

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This study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

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