Publication Date
8-2020
Date of Final Oral Examination (Defense)
6-10-2020
Type of Culminating Activity
Dissertation
Degree Title
Doctor of Philosophy in Electrical and Computer Engineering
Department
Electrical and Computer Engineering
Supervisory Committee Chair
Kurtis D. Cantley, Ph.D.
Supervisory Committee Member
Kristy A. Campbell, Ph.D.
Supervisory Committee Member
Benjamin C. Johnson, Ph.D.
Abstract
In this dissertation, memristor-based spiking neural networks (SNNs) are used to analyze the effect of radiation on the spatio-temporal pattern recognition (STPR) capability of the networks. Two-terminal resistive memory devices (memristors) are used as synapses to manipulate conductivity paths in the network. Spike-timing-dependent plasticity (STDP) learning behavior results in pattern learning and is achieved using biphasic shaped pre- and post-synaptic spikes. A TiO2 based non-linear drift memristor model designed in Verilog-A implements synaptic behavior and is modified to include experimentally observed effects of state-altering, ionizing, and off-state degradation radiation on the device. The impact of neuron “death” (disabled neuron circuits) due to radiation is also examined.
In general, radiation interaction events distort the STDP learning curve undesirably, favoring synaptic potentiation. At lower short-term flux, the network is able to recover and relearn the pattern with consistent training, although some pixels may be affected due to stability issues. As the radiation flux and duration increases, it can overwhelm the leaky integrate-and-fire (LIF) post-synaptic neuron circuit, and the network does not learn the pattern. On the other hand, in the absence of the pattern, the radiation effects cumulate, and the system never regains stability. Neuron-death simulation results emphasize the importance of non-participating neurons during the learning process, concluding that non-participating afferents contribute to improving the learning ability of the neural network. Instantaneous neuron death proves to be more detrimental for the network compared to when the afferents die over time thus, retaining the network’s pattern learning capability.
DOI
10.18122/td/1713/boisestate
Recommended Citation
Dahl, Sumedha Gandharava, "The Effects of Radiation on Memristor-Based Electronic Spiking Neural Networks" (2020). Boise State University Theses and Dissertations. 1713.
10.18122/td/1713/boisestate
Included in
Electronic Devices and Semiconductor Manufacturing Commons, Other Electrical and Computer Engineering Commons