Advanced Photonics Research最新文献

Optoelectronic properties of materials are frequently examined through spectroscopic tools based on the absorption and emission of (visible) photons. Typically, one assumes that these transient characterization processes do not cause observable change of these properties because of the low power of the employed light sources. However, for flexible conjugated polymers in a medium of low viscosity, it is decidedly unclear whether photoexcitation of electrons on the backbone of a long polymer chain has an influence on macromolecular conformations and the corresponding intermolecular interactions. Here, we provide clear experimental evidence that continuous absorption of photons progressively causes persistent changes in the spectroscopic properties of conjugated polymers, accompanied by microscopically observable changes in their spatial distribution within a low-viscosity matrix. By contrast, all these changes were completely absent in nonilluminated parts of the same sample. Quantitative spectral analysis allowed to identify the fraction of molecules that underwent such metamorphosis, which increased with the dose of photoexcitation, that is, with the number of absorbed photons. Complementary experiments revealed the reversibility of this behavior and so demonstrated the integrity of the polymers. Our results depict new pathways for tailoring properties of conjugated polymers and widening their spectrum of advanced engineering applications.

Deep learning (DL) technology has shown extensive potential for modeling radio-over-fiber (RoF) systems. Meanwhile, frequency multiplication technology serves as an effective approach to reduce the bandwidth requirements of core components in RoF links. However, the introduction of harmonic interference, nonlinear distortion, and phase noise in frequency-doubled RoF systems poses additional challenges to DL-based modeling and optimization. A DL-based frequency doubled RoF link end-to-end (E2E) modeling and optimization method is proposed. The DL model consist of three models: a signal preprocessing model for symbol mapping and signal generation, a fiber channel model for transmission link modeling in addition with a receiver model for demodulation. Numerical analysis is carried out based on 16QAM RoF signal with a bit rate of 2 Gbit/s, which indicates that the DL model is capable of modeling the frequency doubled RoF link which incorporates harmonic and nonlinear distortion effects. Moreover, geometry shaping and probabilistic shaping are performed based on E2E framework to further enhance the performance of the system. The results show that the optimized link has better bit error rate performances, indicating that our approach holds potential applications in nonlinear RoF link modeling and could valuable insights for advanced communication systems.

Optoelectronic properties of materials are frequently examined through spectroscopic tools based on the absorption and emission of (visible) photons. Typically, one assumes that these transient characterization processes do not cause observable change of these properties because of the low power of the employed light sources. However, for flexible conjugated polymers in a medium of low viscosity, it is decidedly unclear whether photoexcitation of electrons on the backbone of a long polymer chain has an influence on macromolecular conformations and the corresponding intermolecular interactions. Here, we provide clear experimental evidence that continuous absorption of photons progressively causes persistent changes in the spectroscopic properties of conjugated polymers, accompanied by microscopically observable changes in their spatial distribution within a low-viscosity matrix. By contrast, all these changes were completely absent in nonilluminated parts of the same sample. Quantitative spectral analysis allowed to identify the fraction of molecules that underwent such metamorphosis, which increased with the dose of photoexcitation, that is, with the number of absorbed photons. Complementary experiments revealed the reversibility of this behavior and so demonstrated the integrity of the polymers. Our results depict new pathways for tailoring properties of conjugated polymers and widening their spectrum of advanced engineering applications.

Deep learning (DL) technology has shown extensive potential for modeling radio-over-fiber (RoF) systems. Meanwhile, frequency multiplication technology serves as an effective approach to reduce the bandwidth requirements of core components in RoF links. However, the introduction of harmonic interference, nonlinear distortion, and phase noise in frequency-doubled RoF systems poses additional challenges to DL-based modeling and optimization. A DL-based frequency doubled RoF link end-to-end (E2E) modeling and optimization method is proposed. The DL model consist of three models: a signal preprocessing model for symbol mapping and signal generation, a fiber channel model for transmission link modeling in addition with a receiver model for demodulation. Numerical analysis is carried out based on 16QAM RoF signal with a bit rate of 2 Gbit/s, which indicates that the DL model is capable of modeling the frequency doubled RoF link which incorporates harmonic and nonlinear distortion effects. Moreover, geometry shaping and probabilistic shaping are performed based on E2E framework to further enhance the performance of the system. The results show that the optimized link has better bit error rate performances, indicating that our approach holds potential applications in nonlinear RoF link modeling and could valuable insights for advanced communication systems.

The integration of optical imaging and machine learning on microfluidic platforms makes it possible to achieve high-content, minimally invasive characterization of a population of samples on a single chip. As the analysis of this high-content information is typically conducted in the electronic domain, optoelectronic conversion speeds and the bandwidth available for data processing put a limitation on the throughput of these methods. In this work, we present an analysis system based on diffractive neural networks with the potential for integration in microfluidic systems for high-content classification of objects with high sampling rates. We show that such a system can distinguish objects by size through passive optical inference with a numerical test accuracy of 98.2% and an experimental test accuracy of 83.4%, in an environment compatible with a microfluidic chamber. This development paves the way for novel approaches in high-speed phenotyping of large cell populations based on all-optical or hybrid optoelectronic neuromorphic information processing.

The integration of optical imaging and machine learning on microfluidic platforms makes it possible to achieve high-content, minimally invasive characterization of a population of samples on a single chip. As the analysis of this high-content information is typically conducted in the electronic domain, optoelectronic conversion speeds and the bandwidth available for data processing put a limitation on the throughput of these methods. In this work, we present an analysis system based on diffractive neural networks with the potential for integration in microfluidic systems for high-content classification of objects with high sampling rates. We show that such a system can distinguish objects by size through passive optical inference with a numerical test accuracy of 98.2% and an experimental test accuracy of 83.4%, in an environment compatible with a microfluidic chamber. This development paves the way for novel approaches in high-speed phenotyping of large cell populations based on all-optical or hybrid optoelectronic neuromorphic information processing.

Field enhancement (FE) plays a pivotal role in boosting the four-wave mixing conversion efficiency (FWM CE)in photonic resonant cavities. While previous studies typically attributed this enhancement to intracavity power buildup, fewer have linked it to the effective nonlinear interaction length. In this work, we propose a different perspective that FE can be interpreted as an elongation of effective length. Moreover, we establish a unified framework that bridges nonresonant waveguides and resonant cavities through this generalized effective length concept. We validate the model experimentally using direct-bonded gallium phosphide-on-insulator waveguides and microresonators. We achieve an intrinsic quality (Q) factor of 2.8 × 10⁴ for the TM₀₀ resonance at 1546.15 nm wavelength. The TM₀₀ mode FWM CE is −37.12 dB in a 25 µm-radius microresonator, 18 dB higher than that in a 4.8 mm-long waveguide of identical cross section and polarization under the same on-chip power of 10 dBm. The FWM CEs of both the waveguides and the microresonators exhibit a well-correlated relation versus their effective lengths, confirming the unified theoretical framework. These experimentally validated findings offer a fresh perspective on Kerr nonlinearity and pave the way toward advancing the development of highly efficient nonlinear photonic devices and products.

Field enhancement (FE) plays a pivotal role in boosting the four-wave mixing conversion efficiency (FWM CE)in photonic resonant cavities. While previous studies typically attributed this enhancement to intracavity power buildup, fewer have linked it to the effective nonlinear interaction length. In this work, we propose a different perspective that FE can be interpreted as an elongation of effective length. Moreover, we establish a unified framework that bridges nonresonant waveguides and resonant cavities through this generalized effective length concept. We validate the model experimentally using direct-bonded gallium phosphide-on-insulator waveguides and microresonators. We achieve an intrinsic quality (Q) factor of 2.8 × 10⁴ for the TM₀₀ resonance at 1546.15 nm wavelength. The TM₀₀ mode FWM CE is −37.12 dB in a 25 µm-radius microresonator, 18 dB higher than that in a 4.8 mm-long waveguide of identical cross section and polarization under the same on-chip power of 10 dBm. The FWM CEs of both the waveguides and the microresonators exhibit a well-correlated relation versus their effective lengths, confirming the unified theoretical framework. These experimentally validated findings offer a fresh perspective on Kerr nonlinearity and pave the way toward advancing the development of highly efficient nonlinear photonic devices and products.

Low-Field Non-Reciprocal Terahertz Devices

Indium antimonide (InSb) rod arrays enable robust unidirectional propagation at terahertz frequencies under low magnetic fields (0.1 T). By exploiting the Voigt effect and dispersive scaling, this work realizes magnetic-field-controlled non-reciprocal devices that exhibit topological immunity to defects and sharp bends. More information can be found in the Research Article by Sai Chen and co-workers (10.1002/adpr.202500253).

Low-Field Non-Reciprocal Terahertz Devices

Indium antimonide (InSb) rod arrays enable robust unidirectional propagation at terahertz frequencies under low magnetic fields (0.1 T). By exploiting the Voigt effect and dispersive scaling, this work realizes magnetic-field-controlled non-reciprocal devices that exhibit topological immunity to defects and sharp bends. More information can be found in the Research Article by Sai Chen and co-workers (10.1002/adpr.202500253).