We report on the realization of an on-chip waveguide platform capable of creating arbitrary two-dimensional refractive index profiles in situ and in real-time. The device exhibits complex multimode dynamics which we train to perform machine learning. We tune the refractive index profile in situ using a backpropagation algorithm to perform audio and image classification with up to 50-dimensional inputs. The two-dimensional programmability is realized by sandwiching a photoconductive film and a lithium niobate slab waveguide between two flat electrodes. While applying voltage between the electrodes, we program the effective index of the waveguide by projecting different light patterns onto the photoconductive film. The effective index increases by 10^-3 in illuminated regions via the electro-optic effect, free from any measurable memory effects or cyclic degradation. In conclusion, we developed a photonics platform with versatile spatial programmability that opens new avenues for optical computing and photonic inverse-design.
Photonic neural networks have been developed as a hardware platform to accelerate machine-learning inference. Digital micromirror devices (DMDs) have been playing a critical role in developing a variety of photonic neural networks for their ability to manipulate millions of optical spatial modes in a 2D plane at >10 kHz frame rate. DMDs have not only enabled high-throughput machine-learning inference but also made hardware-in-the-loop training possible with photonic neural networks. In this talk, we will review the functions of DMDs in a plethora of photonic-neural-network architectures and discuss how MEMS-based technologies can enable novel photonic neural networks in future.
Linear optics has been long applied to image compression. However, it is widely known that nonlinear neural networks outperform linear models in terms of feature extraction and image compression. Here, we show a nonlinear multilayer optical neural network using a commercially available image intensifier as a scalable optical-to-optical nonlinear activation function. We experimentally demonstrated that nonlinear ONNs outperform linear optical linear encoders in a variety of non-trivial machine vision tasks at a high image compression ratio (up to 800:1). We have shown that nonlinear ONNs can directly process optical inputs from physical objects under natural illumination, which provides a new pathway towards high-volume, high-throughput, and low-latency machine vision processing.
In conventional approaches to computer-vision tasks such as object recognition, a camera digitally records a high-resolution image and an algorithm is run to extract information from the image. Alternative image-sensing schemes have been proposed that extract high-level features from a scene using optical components, filtering out irrelevant information ahead of conversion from the optical to electronic domains by an array of detectors (e.g., a CMOS image sensor). In this way, images are compressed into a low-dimensional latent space, allowing computer-vision applications to be realized with fewer detectors, fewer photons, and reduced digital post-processing, which enables low-latency processing. Optical neural networks (ONNs) offer a powerful platform for such image compression/feature extraction in the analog, optical domain. While ONNs have been successfully implemented using only linear operations, which can still be leveraged for computer-vision applications, it is well known that adding nonlinearity (a prerequisite for depth) enables neural networks to perform more complex processing. Our work realizes a multilayer ONN preprocessor for image sensing, using a commercial image intensifier as an optoelectronic, optical-to-optical nonlinear activation function. The nonlinear ONN preprocessor achieves compression ratios up to 800:1. At high compression ratios, the nonlinear ONN outperforms any linear preprocessor in terms of classification accuracy on a variety of tasks. Our experiments demonstrate ONN image sensors with incoherent light, but emerging technologies such as metasurfaces, large-scale laser arrays, and novel optoelectronic materials, will provide the means to realize a variety of multilayer ONN preprocessors that act on coherent and/or quantum light.
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