Compact optical convolution processing unit based on multimode interference

PLC-Based Polymer/Silica Hybrid Inverted Ridge LP11 Mode Rotator

The mode rotator is an important component in a PLC-based mode-division multiplexing (MDM) system, which is used to implement high-order modes with vertical intensity peaks, such as LP11b mode conversions from LP11a in PLC chips. In this paper, an LP11 mode rotator based on a polymer/silica hybrid inverted ridge waveguide is demonstrated. The proposed mode rotator is composed of an asymmetrical waveguide with a trench. According to the simulation results, the broadband conversion efficiency between the LP11a and LP11b modes is greater than 98.5%, covering the C-band after optimization. The highest mode conversion efficiency (MCE) is 99.2% at 1550 nm. The large fabrication tolerance of the proposed rotator enables its wide application in on-chip MDM systems.

Experimental demonstration of combination-encoding content-addressable memory of 0.75 bits per switch utilizing Hf-Zr-O ferroelectric tunnel junctions

We experimentally demonstrate the concept of combination-encoding content-addressable memory (CECAM) that offers much higher content density than any other content-addressable memory devices proposed to date. In this work, CECAM was fabricated and validated with a hafnium-zirconium oxide (HZO) ferroelectric tunnel junction (FTJ) crossbar array. The new CAM structure, which utilizes nonvolatile memory devices, offers numerous advantages including low-current operation (FTJ), standby power reduction (ferroelectric HZO), and increased content density. Multibit data are encoded and stored in multi-switch CECAM. Perfect-match searching in CECAM with a reasonable match current (lower than nA) for different sizes of CECAM has been validated from a novel CAM device. We demonstrate N-CECAM (with keys encoded into 2N-long binary arrays) for N = 3 (using 6 FTJs) and 4 (using 8 FTJs), leading to content densities of 0.667 and 0.75 bits per switch, which highlight 33% and 50% increase in content density compared to that of the conventional TCAM (0.5 bits per switch).

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.

Correlated optical convolutional neural network with "quantum speedup"

Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs have been implemented. However, the current ONNs cannot realize the acceleration as powerful as that indicated by the models like quantum neural networks. How to construct and realize an ONN with the quantum speedup is a huge challenge. Here, we propose theoretically and demonstrate experimentally a new type of optical convolutional neural network by introducing the optical correlation. It is called the correlated optical convolutional neural network (COCNN). We show that the COCNN can exhibit "quantum speedup" in the training process. The character is verified from the two aspects. One is the direct illustration of the faster convergence by comparing the loss function curves of the COCNN with that of the traditional convolutional neural network (CNN). Such a result is compatible with the training performance of the recently proposed quantum convolutional neural network (QCNN). The other is the demonstration of the COCNN's capability to perform the QCNN phase recognition circuit, validating the connection between the COCNN and the QCNN. Furthermore, we take the COCNN analog to the 3-qubit QCNN phase recognition circuit as an example and perform an experiment to show the soundness and the feasibility of it. The results perfectly match the theoretical calculations. Our proposal opens up a new avenue for realizing the ONNs with the quantum speedup, which will benefit the information processing in the era of big data.

Noisy image segmentation based on synchronous dynamics of coupled photonic spiking neurons

The collective dynamics in neural networks is essential for information processing and has attracted much interest on the application in artificial intelligence. Synchronization is one of the most dominant phenomenon in the collective dynamics of neural network. Here, we propose to use the spiking dynamics and collective synchronization of coupled photonic spiking neurons for noisy image segmentation. Based on the synchronization mechanism and synchronization control, the noised pattern segmentation is demonstrated numerically. This work provides insight into the possible application based on the collective dynamics of large-scale photonic networks and opens a way for ultra-high speed image processing.