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Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 

Abstract Neuropeptides are abundant signaling molecules in the central nervous system. Yet remarkably little is known about their spatiotemporal spread and biological activity. Here, we developed an integrated optical approach usingPlasmonic nAnovesicles and cell‐based neurotransmitter fluorescent engineered reporter (CNiFER), or PACE, to probe neuropeptide signaling in the mouse neocortex. Small volumes (fL to pL) of exogenously supplied somatostatin‐14 (SST) can be rapidly released under near‐infrared light stimulation from nanovesicles implanted in the brain and detected by SST2 CNiFERs with nM sensitivity. Our measurements reveal reduced but synchronized SST transmission within 130 μm, and markedly smaller and delayed transmission at longer distances. These measurements enabled a quantitative estimation of the SST loss rate due to peptide degradation and binding. PACE offers a new tool for determining the spatiotemporal scales of neuropeptide volume transmission and signaling in the brain.