Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission

Terahertz spoof plasmonic neural network for diffractive information recognition and processing

All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage parallelism and analog computation for complex data processing. However, their low space transmission efficiency or large spatial dimensions hinder miniaturization and broader application. Here, we propose a terahertz spoof plasmonic neural network on a planar diffractive platform for direct multi-target recognition. Our approach employs a spoof surface plasmon polariton coupler array to construct a diffractive network layer, resulting in a compact, efficient, and easily integrable architecture. We designed three schemes: basis vector classification, multi-user recognition, and MNIST handwritten digit classification. Experimental results reveal that the terahertz spoof plasmonic neural network successfully classifies basis vectors, recognizes multi-user orientation information, and directly processes handwritten digits using a designed input framework comprising a metal grating array, transmitters, and receivers. This work broadens the application of terahertz plasmonic metamaterials, paving the way for terahertz on-chip integration, intelligent communication, and advanced computing systems.

Super-resolution diffractive neural network for all-optical direction of arrival estimation beyond diffraction limits

Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN's edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

Ln-HOF Nanofiber Organogels with Time-Resolved Luminescence for Programmable and Reliable Encryption

Developing tunable luminescent materials for high throughput information storage is highly desired following the explosive growth of global data. Although considerable success has been achieved, achieving programmable information encryption remains challenging due to current signal crosstalk problems. Here, we developed long-lived room-temperature phosphorescent organogels enabled by lanthanum-coordinated hydrogen-bonded organic framework nanofibers for time-resolved information programming. Via modulating coassembled lanthanum concentration and Förster resonance energy transfer efficiency, the lifetimes are prolonged and facilely manipulated (20-644 ms), realizing encoding space enlargement and multichannel data outputs. The aggregated strong interfacial supramolecular bonding endows organogels with excellent mechanical toughness (36.16 MJ m-2) and self-healing properties (95.7%), synergistically achieving photostability (97.6% lifetime retention in 10000 fatigue cycles) via suppressing nonradiative decays. This work presents a lifetime-gated information programmable strategy via lanthanum-coordination regulation that promisingly breaks through limitations of current responsive luminescent materials, opening unprecedented avenues for high-level information encryption and protection.

All-analog photoelectronic chip for high-speed vision tasks

Photonic computing enables faster and more energy-efficient processing of vision data1-5. However, experimental superiority of deployable systems remains a challenge because of complicated optical nonlinearities, considerable power consumption of analog-to-digital converters (ADCs) for downstream digital processing and vulnerability to noises and system errors1,6-8. Here we propose an all-analog chip combining electronic and light computing (ACCEL). It has a systemic energy efficiency of 74.8 peta-operations per second per watt and a computing speed of 4.6 peta-operations per second (more than 99% implemented by optics), corresponding to more than three and one order of magnitude higher than state-of-the-art computing processors, respectively. After applying diffractive optical computing as an optical encoder for feature extraction, the light-induced photocurrents are directly used for further calculation in an integrated analog computing chip without the requirement of analog-to-digital converters, leading to a low computing latency of 72 ns for each frame. With joint optimizations of optoelectronic computing and adaptive training, ACCEL achieves competitive classification accuracies of 85.5%, 82.0% and 92.6%, respectively, for Fashion-MNIST, 3-class ImageNet classification and time-lapse video recognition task experimentally, while showing superior system robustness in low-light conditions (0.14 fJ μm-2 each frame). ACCEL can be used across a broad range of applications such as wearable devices, autonomous driving and industrial inspections.

Three-Dimensional Displacement Measurement of Micro-Milling Tool Based on Fiber Array Encoding

The vibration of the micro-milling tool presents a significant chaotic vibration phenomenon, which has a great influence on the tool life and part machining precision, and is one of the basic problems restricting the improvement of machining efficiency and machining accuracy in micro-milling. To overcome the difficulty of the traditional vibration measurement method with the online measurement of micro-milling tool multi-dimensional vibration, a three-dimensional (3D) measurement method of the micro-milling tool is proposed based on multi-fiber array coding, which converts the tool space motion into a decoding process of the optical coding array employing the tool modulating the multi-fiber array encoding. A 6 × 6 optical fiber array was designed, and a 3D motion platform for micro-milling tools was built to verify the characteristics of the optical fiber measurement system. The measurement results show that the measuring accuracy of the system reached 1 µm, and the maximum linear error in x-, y-, and z-direction are 1.5%, 2.58%, and 2.43%, respectively; the tool space motion position measurement results show that the maximum measurement error of the measuring system was 3.4%. The designed system has unique coding characteristics for the tool position in the space of 100 µm³. It provides a new idea and realization means for the online vibration measurement of micro-milling tools.