Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions

Abstract

We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

“…Josephson junctions (JJs) [ 59,61,62,75,208–210 ] and superconducting nanowires [ 211–214 ] are the two principal forces that directly access neuromorphic computing at the device level, with particular implementations often exactly dual with each other. [ 77 ] For instance, the ion channel dynamics of LIF neurons can be efficiently mimicked by two cascading JJs, and the emulation of neuronic relaxation oscillation can be realized by a nanowire resistor incorporation as well.…”

A superconducting nanowire-based architecture for neuromorphic computing

“…By the same token, the tool is not dependent on circuit modelling software such as LTSPICE and uses the more common language of python rather than a higher-level professional language like Verilog A. As a result, the functionality of the tool presented in this work can be similarly extended to other superconducting systems based on Josephson junctions [8,11,17], and quantum phase-slip junctions [13,14] albeit with increased complexity for the component models. Optimizing circuit layouts for power or area given a set of algorithmic constraints would also be an area of extension for the tool.…”

“…Superconducting circuits offer drastically lower power consumption even when cryogenic cooling energy costs is taken into account [6,7]. Previous developments of neuromorphic architectures using superconducting electronics have used Josephson junctions [8][9][10][11], quantum-phase slip junctions [12][13][14], magnetic tunnel junctions [15], systems with Josephson junctions and superconducting nanowire single photon detectors [16,17], and nanowires as relaxation oscillators [7] to construct circuits that emulate biological neurons and synapses. Superconducting nanowires offer ease of fabrication and are most easily integrated with classical circuit elements.…”

A Superconducting Nanowire-based Architecture for Neuromorphic Computing

“…By the same token, the tool is not dependent on circuit modelling software such as LTSPICE and uses a more common design language like python over a higher-level professional language like verilog. As a result, the functionality of the tool presented in this work can be similarly extended to other superconducting systems based on Josephson junctions [7,10,16], and quantum phase-slip junctions [12,13] albeit with increased complexity for the component models. This would be a useful continuation of this work.…”

“…Superconducting circuits offer drastically lower power consumption even when cryogenic cooling is taken into account [6]. Previous developments of neuromorphic architectures using superconducting electronics have used Josephson junctions [7][8][9][10], quantum-phase slip junctions [11][12][13], magnetic tunnel junctions [14], and superconducting nanowires [15][16][17] to construct circuits that emulate biological neurons and synapses. Superconducting nanowires offer ease of fabrication and are most easily integrated with classical circuit elements.…”