Here, we introduce an optical computing method using free-space optics and a 4f system to enhance and integrate data processing, encryption, and machine learning. We propose a Reconfigurable Complex Convolution Module (RCCM) which enables simultaneous amplitude and phase modulation of optical signals for complex convolution operations in the Fourier domain. Utilizing spatial light modulators and interferometric techniques based on the Michelson interferometer, the RCCM achieves precise control over light properties. The system demonstrates promising applications in optical hashing, data compression, and accelerating machine learning tasks, particularly for processing encrypted data. Experimental results show the RCCM’s ability to perform complex convolutions with high accuracy, though trade-offs between compression ratios and classification accuracy are observed. This research represents a significant advancement in optical computing, addressing challenges in data security, processing speed, and computational efficiency across various fields.
Recent advancements in optical communications have explored the use of spatially structured beams, especially orbital angular momentum (OAM) beams, to achieve high-capacity data transmission. Traditional electronic convolutional neural networks (CNNs), while effective, face significant challenges in demultiplexing OAM beams efficiently, notably their high power consumption and substantial computational time, which can limit realtime processing capabilities in high-speed optical communication systems. In this study, we propose a hybrid optical-electronic CNN that integrates Fourier optics convolution for intensity recognition-based demultiplexing of multiplexed OAM beams under simulated atmospheric turbulence. Experimental results showed that the proposed hybrid neural network system achieves a 69% demultiplexing accuracy under strong turbulence conditions while exhibiting a three times reduction in training time compared to all-electronic CNNs. This study underscores the potential of a hybrid optical-electronic neural network to enhance both performance and efficiency in OAM-based optical communication systems.
Here, we present a vanadium carbide (V2C) mid-infrared (mid-IR) photodetector. Drop casting and spin coating a silicon substrate with a thin silicon oxide layer produced the V2C photodetector. Isopropyl alcohol and nitrogen gas drying increased material quality. E-beam lithography and metal deposition of Au/Ti contacts on V2C flakes carefully made electrical connections. Electrical bias and 2 μm laser light evaluated the V2C photodetector’s dark current and photocurrent responses. Photocurrent response changed dramatically, matching FTIR spectroscopy findings. V2C’s peak responsivity of 2.65 A/W demonstrated mid-IR photodetection. To test scalability, we created devices with 2-5 μm channels. For specialized sensing, photocurrent increases with channel length. Onchip waveguides and photonic circuits might use V2C photodetectors. V2C’s mid-IR photodetector exhibits its promise as a cutting-edge optoelectronics and integrated photonics material. This work expands mid-IR-sensing photodetector technology.
Here, we're pioneering a novel approach in photonics, targeting the development of ultra-low power communication systems and advanced sensing technologies. Central to our strategy is the implementation of a unique zig-zag structure, designed to achieve femtojoule (fJ) per bit communication efficiency. A key innovation in our approach is the integration of unidirectional coupling through on-chip isolation, seamlessly connecting a Transverse Coupled Cavity VCSEL (TCCVCSEL) to the modulator and then to a waveguide. This project has wide-ranging implications, extending beyond just creating new devices. It's geared towards establishing a robust III/V platform, serving as a cornerstone in the field of photonics and integrated circuit technology. Our work is poised to catalyze advancements in high-speed, low-power photonic systems, potentially setting new benchmarks in the industry.
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