Abstract:
The ability to achieve spatiotemporal control of spontaneous (thermal and quantum) light emission has been a critical challenge in the field of optics with far ranging applications from remote sensing, holographic displays and quantum information processing. Traditionally, steering spontaneous emission has been deemed near impossible by using phased-array optical components. Here we present our results on ultrafast (sub-ps) dynamic steering of spontaneous emission from high-density InAs quantum dots (QDs) embedded inside an array of GaAs metasurface-resonators. This is demonstrated by dynamically reconfiguring the spatial index profiles on the metasurface using structured optical-pumping. We developed an autonomous experimentation platform to accelerate interpretable scientific discovery in ultrafast nanophotonics, targeting a novel method to steer spontaneous emission from reconfigurable semiconductor metasurfaces. Despite the potential of reconfigurable semiconductor metasurfaces with embedded sources for spatiotemporal control, achieving arbitrary far-field control of spontaneous emission remains challenging. Here, we present a self-driving lab (SDL) platform that addresses this challenge by discovering the governing equations for predicting the far-field emission profile from light-emitting metasurfaces. We discover that both the spatial gradient (grating-like) and the curvature (lens-like) of the local refractive index are key factors in steering spontaneous emission. The SDL employs a machine-learning framework comprising: (1) a variational autoencoder for generating complex spatial refractive index profiles, (2) an active learning agent for guiding experiments with real-time closed-loop feedback, and (3) a neural network-based equation learner to uncover structure-property relationships. The SDL demonstrated a four-fold enhancement in peak emission directivity (up to 77%) over a 74° field of view within ~300 experiments. Our findings reveal that combinations of positive gratings and lenses are as effective as negative lenses and gratings for all emission angles, offering a novel strategy for controlling spontaneous emission beyond conventional Fourier optics. Our discovery of achieving spatio-temporal control of spontaneous emission using the self-driving lab framework has far-reaching applications from remote sensing, quantum information processing, and augmented and virtual reality displays. Ultimately, we have demonstrated an efficient machine learning framework to generate high-dimensional, hypothesis-driven experiments and lead us to interpretable and explainable scientific discoveries without a human-in-the loop.
Bio:
Prasad Iyer is a staff scientist at the center for integrated nanotechnology (CINT) in Sandia National labs. Prasad got his PhD in electrical and computer engineering from the University of California Santa Barbara on reconfigurable optical metasurfaces. He later worked towards commercializing beam steering metasurfaces for Lidars at Lumotive, a Bill Gates funded startup in Seattle before joining Sandia National Lab. Prasad has been a technical advisor for several metamaterial startups and currently leads numerous projects at Sandia and CINT at the intersection of AI-driven discovery of material science, fundamental light-matter interaction and efficient computation with light.
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