Addressing optical modulator non-linearities for photonic neural networks.

Within the context of neuromorphic computing, analog photonics, especially after the advent of photonic integrated technologies, offers unparalleled computing speeds per core, and the reduction of size and power consumption compared to digital electronics. However, the functionality of analog systems is limited by noise and non-linear distortions, which degrade signal resolution. Here, a method is presented for analyzing and minimizing the effect of non-linearities associated with the optical power transfer function of a generic modulator, to inform choices of design and operation conditions. The Mach-Zehnder interferometer, micro-ring modulator, and ring-assisted Mach-Zehnder interferometer are compared using this method. The analysis is applied to compare three analog photonic processor architectures for machine learning applications, based on wavelength, space, and time division multiplexing. Our results indicate that despite the lower maximum resolution exhibited by Mach-Zehnder interferometers, they are the most balanced choice for space and time division multiplexing architectures due to stability and power consumption.