  Multi-plane light converter (MPLC) designs supporting hundreds of modes are
attractive in high-throughput optical communications. These photonic structures
typically comprise >10 phase masks in free space, with millions of independent
design parameters. Conventional MPLC design using wavefront matching updates
one mask at a time while fixing the rest. Here we construct a physical neural
network (PNN) to model the light propagation and phase modulation in MPLC,
providing access to the entire parameter set for optimization, including not
only profiles of the phase masks and the distances between them. PNN training
supports flexible optimization sequences and is a superset of existing MPLC
design methods. In addition, our method allows tuning of hyperparameters of PNN
training such as learning rate and batch size. Because PNN-based MPLC is found
to be insensitive to the number of input and target modes in each training
step, we have demonstrated a high-order MPLC design (45 modes) using mini
batches that fit into the available computing resources.
