Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software systems and electronic neuromorphic models. Unlike computers, the brain uses a unified geometry whereby memory and computation occur in the same physical location. The neuromorphic approach tries to reproduce the functional blocks of biological neural networks. In the photonics field, one possible and efficient way is to use integrated circuits based on soliton waveguides, ie channels self-written by light. Thanks to the nonlinearity of some crystals, propagating light can write waveguides and then can modulate them according to the information it carries. Thus, the created structures are not static but they can self-modify by varying the input information pattern. These hardware systems show a neuroplasticity which is very close to the one which characterize the brain functioning. The solitonic neuromorphic paradigm this work introduces is based on X-junction solitonic neurons as the fundamental elements for complex neural networks. These solitonic units are able to learn information both in supervised and unsupervised ways by unbalancing the X-junction. The storage of information coincides with the evolution of structure that changes plastically. Thus, complex solitonic networks can store information as propagation trajectories and use them for reasoning.

Machine learning has opened a new realm of possibilities in photonic circuit design and manufacturing. First, we describe our approach of using deep learning to optimize the multi-dimensional parameter space for hundreds of optical chips on a mask, resulting in homogeneity of performance in high volume applications. Second, we present our approach of using a support vector machine to predict the performance of optical devices by wafer probing. This approach eliminates the expensive and labour-intensive process of optical chip testing, and allows unprecedented control over the fabrication process, including in-situ monitoring of wafer fabrication and real-time process adjustments. The combination of these two approaches paves the way for accelerated adoption of photonics in high volume applications.