A detailed comparison of the original Gerchberg-Saxton and the Yang-Gu algorithms for the reconstruction of model images from two intensity measurements in a nonunitary transform system is presented. The Yang-Gu algorithm is a generalization of the Gerchberg-Saxton algorithm and is effective in solving the general amplitude-phase-retrieval problem in any linear unitary or nonunitary transform system. For a unitary transform system the Yang-Gu algorithm is identical to the Gerchberg-Saxton algorithm. The reconstruction of images from data corrupted with random noise is also investigated. The simulation results show that the Yang-Gu algorithm is relatively insensitive to the presence of noise in data. In all cases studied the Yang-Gu algorithm always resulted in a highly accurate recovered phase.
Integrated photonic devices based on Si 3 N 4 waveguides allow for the exploitation of nonlinear frequency conversion, exhibit low propagation loss, and have led to advances in compact atomic clocks, ultrafast ranging, and spectroscopy. Yet, the lack of Pockels effect presents a major challenge to achieve high-speed modulation of Si 3 N 4. Here, microwave-frequency acousto-optic modulation is realized by exciting high-overtone bulk acoustic wave resonances (HBAR) in the photonic stack. Although HBAR is ubiquitously used in modern communication and superconducting circuits, this is the first time it has been incorporated on a photonic integrated chip. The tight vertical acoustic confinement releases the lateral design of freedom, and enables negligible cross-talk and preserving low optical loss. This hybrid HBAR nanophotonic platform can find immediate applications in topological photonics with synthetic dimensions, compact opto-electronic oscillators, and microwave-to-optical converters. As an application, a Si 3 N 4-based optical isolator is demonstrated by spatiotemporal modulation, with over 17 dB isolation achieved.
Recent advances in silicon photonic chips have made huge progress in optical computing owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural networks serves as an alternative for mitigating the rapidly increased demand for computing resources in electronic platforms. However, it remains a formidable challenge to train the online programmable optical neural networks efficiently, being restricted by the difficulty in obtaining gradient information on a physical device when executing a gradient descent algorithm. Here, we experimentally demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. A gradient-free algorithm, that is, the genetic algorithm, is adopted. The protocol is on-chip implementable, physical agnostic (no need to rely on characterization and offline modeling), and gradientfree. The protocol works for various types of chip structures and is especially helpful to those that cannot be analytically decomposed and characterized. We confirm its viability using several practical tasks, including the crossbar switch and the Iris classification. Finally, by comparing our physics-agonistic and gradient-free method to the off-chip and gradient-based training methods, we demonstrate the robustness of our system to perturbations such as imperfect phase implementation and photodetection noise. Optical processors with gradient-free genetic algorithms have broad application potentials in pattern recognition, reinforcement learning, quantum computing, and realistic applications (such as facial recognition, natural language processing, and autonomous vehicles).
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