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Programmable photonic integrated circuits (PICs) consisting of reconfigurable on-chip optical components have been creating new paradigms in various applications, such as integrated spectroscopy, multi-purpose microwave photonics, and optical information processing. Among many reconfiguration mechanisms, non-volatile chalcogenide phase-change materials (PCMs) exhibit a promising approach to the future very-large-scale programmable PICs, thanks to their zero static power and large optical index modulation, leading to extremely low energy consumption and ultra-compact footprints. However, the scalability of the current PCM-based programmable PICs is still limited since they are not directly off-the-shelf in commercial photonic foundries now. Here, we demonstrate a scalable platform harnessing the mature and reliable 300 mm silicon photonic fab, assisted by an in-house wide-bandgap PCM (Sb₂S₃) integration process. We show various non-volatile programmable devices, including micro-ring resonators, Mach-Zehnder interferometers and asymmetric directional couplers, with low loss (~0.0044 dB/µm), large phase shift (~0.012 π/µm) and high endurance (>5000 switching events with little performance degradation). Moreover, we showcase this platform’s capability of handling relatively complex structures such as multiple PIN diode heaters in devices, each independently controlling an Sb₂S₃segment. By reliably setting the Sb₂S₃segments to fully amorphous or crystalline state, we achieved deterministic multilevel operation. An asymmetric directional coupler with two unequal-length Sb₂S₃segments showed the capability of four-level switching, beyond cross-and-bar binary states. We further showed unbalanced Mach-Zehnder interferometers with equal-length and unequal-length Sb₂S₃segments, exhibiting reversible switching and a maximum of 5 ($$N+1,N=4$$$N+1,N=4$) and 8 ($${2}^{N},N=3$$$2^N,N=3$) equally spaced operation levels, respectively. This work lays the foundation for future programmable very-large-scale PICs with deterministic programmability.

The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era.

Quantitative phase imaging (QPI) recovers the exact wavefront of light from intensity measurements. Topographical and optical density maps of translucent microscopic bodies can be extracted from these quantified phase shifts. We demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid metalens. Our method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color camera. Our 0.5 mm aperture metalens shows robust quantitative phase imaging capability with a$${28}^{\circ}$$${28}^{\circ }$field of view and 0.$${2}{\pi}$$$2\pi$phase resolution ( ~ 0.$${1}{\lambda}$$$1\lambda$in air) for experiments with an endoscopic fiber bundle. Since the spectral functionality is encoded directly in the imaging lens, the metalens acts both as a focusing element and a spectral filter. The use of a simple computational backend will enable real-time operation. Key limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition, interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.

Subwavelength diffractive optics known as meta-optics have demonstrated the potential to significantly miniaturize imaging systems. However, despite impressive demonstrations, most meta-optical imaging systems suffer from strong chromatic aberrations, limiting their utilities. Here, we employ inverse-design to create broadband meta-optics operating in the long-wave infrared (LWIR) regime (8-12μm). Via a deep-learning assisted multi-scale differentiable framework that links meta-atoms to the phase, we maximize the wavelength-averaged volume under the modulation transfer function (MTF) surface of the meta-optics. Our design framework merges local phase-engineering via meta-atoms and global engineering of the scatterer within a single pipeline. We corroborate our design by fabricating and experimentally characterizing all-silicon LWIR meta-optics. Our engineered meta-optic is complemented by a simple computational backend that dramatically improves the quality of the captured image. We experimentally demonstrate a six-fold improvement of the wavelength-averaged Strehl ratio over the traditional hyperboloid metalens for broadband imaging.

Correlated quantum many-body phenomena in lattice models have been identified as a set of physically interesting problems that cannot be solved classically. Analog quantum simulators, in photonics and microwave superconducting circuits, have emerged as near-term platforms to address these problems. An important ingredient in practical quantum simulation experiments is the tomography of the implemented Hamiltonians—while this can easily be performed if we have individual measurement access to each qubit in the simulator, this could be challenging to implement in many hardware platforms. In this paper, we present a scheme for tomography of quantum simulators which can be described by a Bose-Hubbard Hamiltonian while having measurement access to only some sites on the boundary of the lattice. We present an algorithm that uses the experimentally routine transmission and two-photon correlation functions, measured at the boundary, to extract the Hamiltonian parameters at the standard quantum limit. Furthermore, by building on quantum enhanced spectroscopy protocols that, we show that with the additional ability to switch on and off the on-site repulsion in the simulator, we can sense the Hamiltonian parameters beyond the standard quantum limit.