Spatially resolved multimode excitation for smooth supercontinuum generation in a SiN waveguide.

We propose a method of supercontinuum light generation enhanced by multimode excitation in a precisely dispersion-engineered deuterated SiN (SiN:D) waveguide. Although a regularly designed SiN-based nonlinear optical waveguide exhibits anomalous dispersion with the fundamental and first-order multimode operation, the center-symmetric light pumping at the input edge has so far inhibited the full potential of the nonlinearity of SiN-based materials. On the basis of numerical analysis and simulation for the SiN:D waveguide, we intentionally applied spatial position offsets to excite the fundamental and higher-order modes to realize bandwidth broadening with flatness. Using this method, we achieved an SNR improvement of up to 18 dB at a wavelength of 0.6 µm with an offset of about 1 µm in the Y-axis direction and found that the contribution was related to the presence of dispersive waves due to the excitation of TE10, and TE01 modes.

Direct f-3f self-referencing using an integrated silicon-nitride waveguide.

We have achieved the simultaneous generation of a 2.6-octave-wide supercontinuum (SC) spectrum over 400-2500 nm and third-harmonic light solely by a dispersion-controlled silicon-nitride waveguide (SiNW). To increase the visible intensity of the SC light component, we fabricated low-loss 5-mm-long deuterated SiNWs with spot-size converters by low-temperature deposition. We succeeded in measuring the carrier-envelope-offset (CEO) signal with a 34-dB signal-to-noise ratio because this short deuterated SiNW provides a large temporal overlap between the f and 3f components. In addition, we have demonstrated this method of CEO locking at telecommunications wavelengths with f-3f self-referencing generated solely by the SiNW without the use of highly nonlinear fiber and an additional nonlinear crystal. Compared with the method of CEO locking with a highly nonlinear fiber and a standard f-2f self-referencing interferometer, this method is not only simple and compact but also stable.

Arbitrary reconfiguration of universal silicon photonic circuits by bacteria foraging algorithm to achieve reconfigurable photonic digital-to-analog conversion.

Reconfigurable/reprogrammable universal silicon photonic circuits represent a paradigm shift in designing photonic devices. However, it is very challenging to perform adaptive arbitrary reconfiguration when the high-dimensional solution of phase distribution cannot be explicitly determined, especially when there are random initial phase errors, which hinder the implementation of novel potential functions in universal circuits. This work presents an arbitrary black-box reconfiguration for universal circuits with random phase errors by a bacteria-foraging algorithm and unlocks a novel function of arbitrary-port-and-arbitrary-bit-resolution reconfigurable 6-bit photonic digital-to-analog conversion. This work offers a general and efficient method to ease multipurpose reconfiguration for universal silicon photonic circuits.

On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification.

On-chip training remains a challenging issue for photonic devices to implement machine learning algorithms. Most demonstrations only implement inference in photonics for offline-trained neural network models. On the other hand, artificial neural networks are one of the most deployed algorithms, while other machine learning algorithms such as supporting vector machine (SVM) remain unexplored in photonics. Here, inspired by SVM, we propose to implement projection-based classification principle by constructing nonlinear mapping functions in silicon photonic circuits and experimentally demonstrate on-chip bacterial foraging training for this principle to realize single Boolean logics, combinational Boolean logics, and Iris classification with ~96.7 - 98.3 per cent accuracy. This approach can offer comparable performances to artificial neural networks for various benchmarks even with smaller scales and without leveraging traditional activation functions, showing scalability advantage. Natural-intelligence-inspired bacterial foraging offers efficient and robust on-chip training, and this work paves a way for photonic circuits to perform nonlinear classification.

Photonic crystal directional coupler switch with small switching length and wide bandwidth.

A directional coupler switch structure capable of short switching length and wide bandwidth is proposed. The switching length and bandwidth have a trade-off relationship in conventional directional coupler switches. Dispersion curves that avoid this trade-off are derived, and a two-dimensional photonic crystal structure that achieves these dispersion curves is presented. Numerical calculations show that the switching length of the proposed structure is 7.1% of that for the conventional structure, while the bandwidth is 2.17 times larger.