  Spiking neural network offers the most bio-realistic approach to mimic the
parallelism and compactness of the human brain. A spiking neuron is the central
component of an SNN which generates information-encoded spikes. We present a
comprehensive design space analysis of the superconducting memristor (SM)-based
electrically reconfigurable cryogenic neuron. A superconducting nanowire (SNW)
connected in parallel with an SM function as a dual-frequency oscillator and
two of these oscillators can be coupled to design a dynamically tunable spiking
neuron. The same neuron topology was previously proposed where a fixed
resistance was used in parallel with the SNW. Replacing the fixed resistance
with the SM provides an additional tuning knob with four distinct combinations
of SM resistances, which improves the reconfigurability by up to ~70%.
Utilizing an external bias current (Ibias), the spike frequency can be
modulated up to ~3.5 times. Two distinct spike amplitudes (~1V and ~1.8 V) are
also achieved. Here, we perform a systematic sensitivity analysis and show that
the reconfigurability can be further tuned by choosing a higher input current
strength. By performing a 500-point Monte Carlo variation analysis, we find
that the spike amplitude is more variation robust than spike frequency and the
variation robustness can be further improved by choosing a higher Ibias. Our
study provides valuable insights for further exploration of materials and
circuit level modification of the neuron that will be useful for system-level
incorporation of the neuron circuit
