Quantitative comparison of the computational complexity of optical, digital and hybrid neural network architectures for image classification tasks.

By implementing neuromorphic paradigms in processing visual information, machine learning became crucial in an ever-increasing number of applications of our everyday lives, ever more performing but also computationally demanding. While a pre-processing of the information passively in the optical domain, before optical-electronic conversion, can reduce the computational requirements for a machine learning task, a comprehensive analysis of computational requirements for hybrid optical-digital neural networks is thus far missing. In this work we critically compare and analyze the performance of different optical, digital and hybrid neural network architectures with respect to their classification accuracy and computational requirements for analog classification tasks of different complexity.

Ridge resonators with compact guided mode coupling.

Ridge resonators are a recently introduced integrated photonic circuit element based on bound states in the continuum (BICs) which can produce a single, sharp resonance over a broad wavelength range with high extinction ratio. However, to excite these resonators, a broad beam of laterally unbound slab mode is required, resulting in a large device footprint, which is not attractive for integrated photonic circuits. In this contribution, we propose and numerically validate a guided-mode waveguide structure that can be analogue to the BIC-based ridge resonators.

Ridge waveguide couplers with leaky mode resonator-like wavelength responses.

Integrated photonic resonators based on bound states in the continuum (BICs) on the silicon-on-insulator (SOI) platform have the potential for novel, mass-manufacturable resonant devices. While the nature of BIC-based ridge resonators requires the resonators to be extended in the (axial) propagation direction of the resonant mode, the requirement for excitation from the quasi-continuum extends the resonator structures also in the lateral dimensions, resulting in large device footprints. To overcome this footprint requirement, we investigate the translation of BIC-based ridge resonators into a guided mode system with finite lateral dimensions.

Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks.

Retrieving the pupil phase of a beam path is a central problem for optical systems across scales, from telescopes, where the phase information allows for aberration correction, to the imaging of near-transparent biological samples in phase contrast microscopy. Current phase retrieval schemes rely on complex digital algorithms that process data acquired from precise wavefront sensors, reconstructing the optical phase information at great expense of computational resources. Here, we present a compact optical-electronic module based on multi-layered diffractive neural networks printed on imaging sensors, capable of directly retrieving Zernike-based pupil phase distributions from an incident point spread function.

Ridge resonators: impact of excitation beam and resonator losses.

Photonic resonators based on bound states in the continuum are attractive for sensing and telecommunication applications, as they have the potential to achieve ultra-high Q-factor resonators in a compact footprint. Recently, ridge resonators - leaky mode resonators based on a bound state in the continuum - have been demonstrated on a scalable photonic integrated circuit platform. However, high Q-factor ridge resonators have thus far not been achieved.

Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip.

Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption.