  Neural networks and neuromorphic computing play pivotal roles in deep
learning and machine vision. Due to their dissipative nature and inherent
limitations, traditional semiconductor-based circuits face challenges in
realizing ultra-fast and low-power neural networks. However, the spiking
behavior characteristic of single flux quantum (SFQ) circuits positions them as
promising candidates for spiking neural networks (SNNs). Our previous work
showcased a JJ-Soma design capable of operating at tens of gigahertz while
consuming only a fraction of the power compared to traditional circuits, as
documented in [1]. This paper introduces a compact SFQ-based synapse design
that applies positive and negative weighted inputs to the JJ-Soma. Using an
RSFQ synapse empowers us to replicate the functionality of a biological neuron,
a crucial step in realizing a complete SNN. The JJ-Synapse can operate at
ultra-high frequencies, exhibits orders of magnitude lower power consumption
than CMOS counterparts, and can be conveniently fabricated using commercial Nb
processes. Furthermore, the network's flexibility enables modifications by
incorporating cryo-CMOS circuits for weight value adjustments. In our endeavor,
we have successfully designed, fabricated, and partially tested the JJ-Synapse
within our cryocooler system. Integration with the JJ-Soma further facilitates
the realization of a high-speed inference SNN.
