Rapid Calibration of High-Performance Wavelength Selective Switches Based on the Few-Shot Transfer Learning

Wavelength selective switches (WSSs) have been widely deployed in reconfigurable optical add/drop multiplexers (ROADM) networks. The demand for better and more intelligent connectivity drives WSS technology to higher port count, wider spectral coverage and better performance. Due to the complicated fringing field effect within the switching engine, i.e. the liquid crystal on silicon (LCOS) device, of the WSS modules, this requires sophisticated design and calibration of the beam-steering holograms. This process is time-consuming and requires expensive testing equipment, contributing to a significant portion of the manufacturing costs. In this paper, we demonstrated a few-shot transfer learning method that was able to speed up this calibration and optimisation process while meeting the stringent performance requirement. In the proposed process, a digital twin model of the WSS systems was developed to generate the dataset for the training of a convolutional recurrent neural network (RNN). This pre-trained neural network was able to simulate the optimised hologram designs for all the switching scenarios. Subsequently, a very limited experimental characterisation of the actual WSS was performed to update this pre-trained model to reflect the inevitable discrepancy between the digital twin model and the actual system. It was demonstrated that transfer learning reduced the experimental calibration workload by 88% and the training time by 30% while achieving >25 dB worst-case port isolation levels across all the switching scenarios.