In this work, we propose an ultra-broadband and ultra-compact polarization beam splitter (PBS) on a standard silicon-on-isolator platform. Assisted by a tapered subwavelength-grating waveguide and a slot waveguide, the working bandwidth of the directional-coupler-based PBS covers the entire O-, E-, S-, C-, L- and U-bands and the coupling length is only 4.6 µm. The insertion losses (ILs) of the device are simulated to be less than 0.8 dB and the extinction ratios (ERs) are larger than 10.9 dB at the wavelength range of 1260-1680 nm for both TE and TM polarizations. The experimental results show the average ILs are less than 1 dB for both polarizations at our measured wavelength ranges, which are consistent with the simulation results. It has the largest 1-dB bandwidth among all the reported broadband PBSs to the best of our knowledge.
We design, fabricate, and characterize a compact dual-mode waveguide crossing on a silicon-on-insulator platform. The dual-mode waveguide crossing with high performance is designed by utilizing the adjoint shape optimization. This adjoint-method-based optimization algorithm is computationally efficient and yields the optimal solution in fewer iterations compared with other iterative schemes. Our proposed dual-mode waveguide crossing exhibits low insertion loss and low crosstalk. Experimental results show that the insertion losses at the wavelength of 1550 nm are 0.83 dB and 0.50 dB for TE0 and TE1 modes, respectively. The crosstalk is less than -20 dB for the two modes over a wavelength range of 80 nm. The footprint of the whole structure is only 5 × 5 μm2.
Abstract Convolutional neural networks are a powerful category of artificial neural networks that can extract features from raw data to provide greatly reduced parametric complexity and enhance pattern recognition and the accuracy of prediction. Optical neural networks offer the promise of dramatically accelerating computing speed while maintaining low power consumption even when using high‐speed data streams running at hundreds of gigabit/s. Here, we propose an optical convolutional processor (CP) that leverages the spectral response of an arrayed waveguide grating (AWG) to enhance convolution speed by eliminating the need for repetitive element‐wise multiplication. Our design features a balanced AWG configuration, enabling both positive and negative weightings essential for convolutional kernels. A proof‐of‐concept demonstration of an 8‐bit resolution processor is experimentally implemented using a pair of AWGs with a broadband Mach–Zehnder interferometer (MZI) designed to achieve uniform weighting across the whole spectrum. Experimental results demonstrate the CP's effectiveness in edge detection and achieved 96% accuracy in a convolutional neural network for MNIST recognition. This approach can be extended to other common operations, such as pooling and deconvolution in Generative Adversarial Networks. It is also scalable to more complex networks, making it suitable for applications like autonomous vehicles and real‐time video recognition.
We propose a coarse wavelength division (de)multiplexer by cascading wavelength filters. Assisted by topology optimization, four compact wavelength filters centered at different wavelengths are designed with less than -0.7 dB insertion loss, respectively.
We propose a dual-layer polarization splitting grating coupler (PSGC) in multilayer silicon nitride-on-silicon photonic circuits. The coupling efficiency reaches −3.2 dB with polarization dependent loss (PDL) of 0.08 dB at 1550 nm.
Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.
We designed a polarization splitter and rotator via topology optimization. The conversion loss is lower than 1.2 dB at the wavelength range of C-band within 6×2 μm 2 footprint.
We proposed an inverse-designed compact half adder on a silicon-on-insulator platform with a footprint of 2µm×2µm. The optical power of SUM and CARRY is controlled by different input combinations, according to the truth table of a half adder. Topology optimization is applied to cope with multiple objective functions in such a combinational logic circuit. The transmittance at 1550 nm for CARRY with 11 input is 170.2%, with extinction ratios (ERs) of 27.1 and 5.8 dB for SUM and CARRY, respectively. The SUM and CARRY outputs have ERs over 22.0 dB and 5.7 dB from 1515 nm to 1600 nm. Phase condition and morphology analysis show that the device has high tolerance on phase fluctuation and fabrication. The proposed device with compact footprint, low insertion loss, and large bandwidth presents a novel, to the best of our knowledge, approach to achieve all-optical combinational logic circuits with inverse design.
Fiber couplers usually take a lot of space on photonic integrated circuits due to the large mode-size mismatch between the waveguide and fiber, especially when a fiber with larger core is utilized, such as a few-mode fiber. We demonstrate experimentally that such challenge can be overcome by an ultra-compact mode-size converter with a footprint of only 10 µm. Our device expands TE0 and TE1 waveguide modes simultaneously from a 1-µm wide strip waveguide to an 18-µm wide slab on a 220-nm thick silicon-on-insulator, with calculated losses of 0.75 dB and 0.68 dB, respectively. The fabricated device has a measured insertion loss of 1.02 dB for TE0 mode and 1.59 dB for TE1 mode. By connecting the ultra-compact converter with diffraction grating couplers, higher-order modes in a few-mode fiber can be generated with a compact footprint on-chip.
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