  Neuromorphic photonic processors based on resonator weight banks are an
emerging candidate technology for enabling modern artificial intelligence (AI)
in high speed, analog systems. These purpose-built analog devices implement
vector multiplications with the physics of resonator devices, offering
efficiency, latency, and throughput advantages over equivalent electronic
circuits. Along with these advantages, however, often comes the difficult
challenges of compensation for fabrication variations and environmental
disturbances. In this paper we review sources of variation and disturbances
from our experiments, as well as mathematically define quantities that model
them. Then, we introduce how the physics of resonators can be exploited to
weight and sum multiwavelength signals. Finally, we outline automated design
and control methodologies necessary to create practical, manufacturable, and
high accuracy/precision resonator weight banks that can withstand operating
conditions in the field. This represents a road map for unlocking the potential
of resonator weight banks in practical deployment scenarios.
