Nanoprinted Diffractive Layer Integrated Vertical-Cavity Surface-Emitting Vortex Lasers with Scalable Topological Charge

Dong, Yibo; Pan, Guanzhong; Xun, Meng; Su, Hang; Chen, Long; Sun, Yun; Luan, Haitao; Fang, Xinyuan; Wu, Dexin; Gu, Min

doi:10.1021/acs.nanolett.3c02938

Cited by 8 publications

(4 citation statements)

References 57 publications

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“…Besides, we can also consider building a 3D-integrated DNN system based on the vertical-cavity surface-emitting lasers (VCSELs) 29 . VCSELs are micron-sized on-chip light sources that emit light perpendicular to the substrate 50 , so VCSEL can be used to generate optical images by constructing a two-dimensional addressable array. The DNNs can be directly integrated on the surface of VCSELs through heterogeneous bonding 51 .…”

Section: Discussionmentioning

confidence: 99%

Compact eternal diffractive neural network chip for extreme environments

Dong,

Lin,

Chen

et al. 2024

Commun Eng

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Artificial intelligence applications in extreme environments place high demands on hardware robustness, power consumption, and speed. Recently, diffractive neural networks have demonstrated superb advantages in high-throughput light-speed reasoning. However, the robustness and lifetime of existing diffractive neural networks cannot be guaranteed, severely limiting their compactness and long-term inference accuracy. Here, we have developed a millimeter-scale and robust bilayer-integrated diffractive neural network chip with virtually unlimited lifetime for optical inference. The two diffractive layers with binary phase modulation were engraved on both sides of a quartz wafer. Optical inference of handwritten digital recognition was demonstrated. The results showed that the chip achieved 82% recognition accuracy for ten types of digits. Moreover, the chip demonstrated high-performance stability at high temperatures. The room-temperature lifetime was estimated to be 1.84×1023 trillion years. Our chip satisfies the requirements for diffractive neural network hardware with high robustness, making it suitable for use in extreme environments.

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Section: Discussionmentioning

confidence: 99%

Compact eternal diffractive neural network chip for extreme environments

Dong,

Lin,

Chen

et al. 2024

Commun Eng

Self Cite

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“…To reduce the expenses of the system, the SLMs can be replaced by the passive optical elements, such as the compact planar optical elements based on metasurface holograms 51 , 52 and patterned liquid crystals holograms 53 , 54 . To achieve the high degree of the device integration, one can adopt nanophotonic and optoelectronic devices with high spatial resolution and physical connectivity to construct the on-chip CNN with a high neuron density 55 – 57 . In terms of the parallelism, the large-area diffractive devices are also desired to achieve high-efficient all-optical machine learning-based information mode-feature encoding 58 .…”

Section: Discussionmentioning

confidence: 99%

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Fang,

Hu,

et al. 2024

Light Sci Appl

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Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction, and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding, leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes, as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover, through extending the target encoded modes into multiplexed OAM states, we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis, which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

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“…The cover glass with the CE lens is then cut to smaller size to fit the CMOS chip using a UV curable adhesive (figure 7(d)). In addition, the optical elements can also be fabricated on the surface of light sources such as vertical-cavity surface-emitting lasers to perform wavefront modulation directly [68]. Such TPL fabricated systems are superior to imaging systems realized using other fabrication techniques as they enable miniaturization, give precise control on the structure profile and deliver high imaging quality.…”

Section: Fabrication On Flat Substratesmentioning

confidence: 99%

Two-photon polymerization lithography for imaging optics

Wang,

Pan,

et al. 2024

Int. J. Extrem. Manuf.

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Optical imaging systems have greatly extended human visual capabilities, enabling the observation and understanding of diverse phenomena. Imaging technologies span a broad spectrum of wavelengths from X-ray to radio frequencies and impact research activities and our daily lives. Traditional glass lenses are fabricated through a series of complex processes, while polymers offer versatility and ease of production. However, modern applications often require complex lens assemblies, driving the need for miniaturization and advanced designs with micro- and nanoscale features to surpass the capabilities of traditional fabrication methods. Three-dimensional (3D) printing, or additive manufacturing, presents a solution to these challenges with benefits of rapid prototyping, customized geometries, and efficient production, particularly suited for miniaturized optical imaging devices. Various 3D printing methods have demonstrated advantages over traditional counterparts, yet challenges remain in achieving nanoscale resolutions. Two-photon polymerization lithography (TPL), a nanoscale 3D printing technique, enables the fabrication of intricate structures beyond the optical diffraction limit via the nonlinear process of two-photon absorption within liquid resin. It offers unprecedented abilities, e.g., alignment-free fabrication, micro- and nanoscale capabilities, and rapid prototyping of almost arbitrary complex 3D nanostructures. In this review, we emphasize the importance of the criteria for optical performance evaluation of imaging devices, discuss material properties relevant to TPL, fabrication techniques, and highlight the application of TPL in optical imaging. As the first panoramic review on this topic, it will equip researchers with foundational knowledge and recent advancements of TPL for imaging optics, promoting a deeper understanding of the field. By leveraging on its high-resolution capability, extensive material range, and true 3D processing, alongside advances in materials, fabrication, and design, we envisage disruptive solutions to current challenges and a promising incorporation of TPL in future optical imaging applications.

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Nanoprinted Diffractive Layer Integrated Vertical-Cavity Surface-Emitting Vortex Lasers with Scalable Topological Charge

Cited by 8 publications

References 57 publications

Compact eternal diffractive neural network chip for extreme environments

Compact eternal diffractive neural network chip for extreme environments

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Two-photon polymerization lithography for imaging optics

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