Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character ‘B’ by separating correlated and uncorrelated afferents. The network uses spike-timing-dependent plasticity (STDP) learning behavior, which is implemented using biphasic neuron spikes. A TiO2 memristor non-linear drift model is used to simulate synaptic behavior in the neuromorphic circuit. The network uses a many-to-one topology with 25 pre-synaptic neurons (afferent) each connected to a memristive synapse and one post-synaptic neuron. The memristor model is modified to include the experimentally observed effect of state-altering radiation. During the learning process, irradiation of the memristors alters their conductance state, and the effect on circuit learning behavior is determined. Radiation is observed to generally increase the synaptic weight of the memristive devices, making the network connections more conductive and less stable. However, the network appears to relearn the pattern when radiation ceases but does take longer to resolve the correlation and pattern. Network recovery time is proportional to flux, intensity, and duration of the radiation. Further, at lower but continuous radiation exposure, (flux 1x1010 cm−2 s−1 and below), the circuit resolves the pattern successfully for up to 100 s

Calculations of Vacancy Diffusivity in WO3

The memristor is viewed as a promising material to store digital information and has analog applications that drew researchers’ attention. Researchers explored the possibilities of using memristors to simulate synapses in the human brain. WO3 is one of the materials that can make memristors. Based on the mechanism of memristors, we know the motion of defects in WO3 changes the Schottky barrier and the current; thus, it can make the switch between high resistance state, HRS, and low resistance state, LRS. This paper will explore vacancy diffusivity in WO3. In this research, we concentrate on the cubic and monoclinic structure of WO3. We use the first principle density functional theory or DFT, and hybrid DFT to calculate the formation energy of different charge states of oxygen vacancies in WO3 and plot the graph of Fermi level to find the charge state with the lowest formation energy conditions. We use the nudged elastic band method to get the energy barrier for the vacancies to migrate inside the structure

The Effects of Radiation on Memristor-Based Electronic Spiking Neural Networks

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

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

In this paper, a feed-forward memristor-based spiking neural network is taught to separate correlated and uncorrelated synapse and learn character inputs using spike-timing-dependent plasticity (STDP). A TiO2 non-linear drift memristor model is used to simulate a neuromorphic circuit with 25 pre- and 1 post-synaptic neuron. During the learning process, memristors are radiated with state-altering radiation and the effect on circuit learning behavior is determined. It is observed that the network recovers when radiation ceases but takes longer to resolve the correlation. Further, at lower but continuous radiation exposure, the circuit may resolve the pattern indefinitely