Fabrication error tolerant broadband mode converters and their working principles.

Computational inverse design techniques have shown potential to become reliable means for designing compact nanophotonic devices without compromising the performance. Much effort has been made to reduce the computation cost involved in the optimization process and obtain final designs that are robust to fabrication imperfections. In this work, we experimentally demonstrate TE0-TE1 and TE1-TE3 mode converters (MCs) on the silicon-on-insulator platform designed using the computationally efficient shape optimization method. These MCs have mode conversion efficiencies above 95%, and the insertion loss ranges from 0.3 dB to 1 dB over a wavelength span of 80 nm ranging from 1.5 µm to 1.58 µm. Maximum modal crosstalk found experimentally in the C-band is -19 dB. The conversion efficiency drops at most by 2.2% at 1.55 µm for 10 nm over/under etch, implying good robustness to dimensional variations. We present the mode conversion mechanism of these MCs by studying the simulated electromagnetic field patterns and validate with supportive data. We also demonstrate their performance in the time domain with a 28 Gbps OOK and a 20 GBaud PAM-4 payload transmissions, which supports their utility for high throughput data communications. The open eye diagrams exhibit Q-factors of 8 dB.

Efficient mode exchanger-based silicon photonic switch enabled by inverse design.

A novel and energy efficient mode insensitive switch building block is proposed and experimentally demonstrated on a silicon-on-insulator platform. Based on a Mach-Zehnder interferometer, the switch uses a relatively compact mode insensitive phase shifter which includes a mode exchanger. The novel structure realizes the exact same phase shift for all modes by exchanging the modes midway within the phase shifter. The design approach leads to reduced power consumption otherwise not possible. Switching the first two quasi transverse electric (TE) modes simultaneously consumes 25.6 mW of power, an approximately 30% reduction from previous reported demonstrations. The measured insertion loss is 3.1 dB on average with a worst-case crosstalk of -14.9 dB over a 40 nm optical bandwidth from 1530 nm to 1570 nm. The design methodology enables scalability up to four optical modes.

Efficient Bloch mode calculation of periodic systems with arbitrary geometry and open boundary conditions in the complex wavevector domain.

We show how existing iterative methods can be used to efficiently and accurately calculate Bloch periodic solutions of Maxwell's equations in arbitrary geometries. This is carried out in the complex-wavevector domain using a commercial frequency-domain finite-element solver that is available to the general user. The method is capable of dealing with leaky Bloch mode solutions, and is extremely efficient even for 3D geometries with non-trivial material distributions. We perform independent finite-difference time-domain simulations of Maxwell's equations to confirm our results. This comparison demonstrates that the iterative mode finder is more accurate, since it provides the true solutions in the complex-wavevector domain and removes the need for additional signal processing and fitting. Due to its efficiency, generality and reliability, this technique is well suited for complex and novel design tasks in integrated photonics, and also for a wider range of photonics problems.

Topological inverse design of nanophotonic devices with energy constraint.

In this paper, we introduce an energy constraint to improve topology-based inverse design. Current methods typically place the constraints solely on the device geometry and require many optimization iterations to converge to a manufacturable solution. In our approach the energy constraint directs the optimization process to solutions that best contain the optical field inside the waveguide core medium, leading to more robust designs with relatively larger minimum feature size. To validate our method, we optimize two components: a mode converter (MC) and a wavelength demultiplexer. In the MC, the energy constraint leads to nearly binarized structures without applying independent binarization stage. In the demultiplexer, it also reduces the appearance of small features. Furthermore, the proposed constraint improves the robustness to fabrication imperfections as shown in demultiplexer design. With energy constraint optimization, the corresponding spectrum shifts under ±10 nm dimensional variations are reduced by 17% to 30%. The proposed constraint is unique in simultaneously taking both geometry and electric field into account, opening the door to new ideas and insights to further improve the computationally intensive topology-based optimization process of nanophotonic devices.

Perfectly vertical surface grating couplers using subwavelength engineering for increased feature sizes.

We present perfectly vertical grating couplers for the 220 nm silicon-on-insulator platform incorporating subwavelength metamaterials to increase the minimum feature sizes and achieve broadband low back-reflection. Our study reveals that devices with high coupling efficiencies are distributed over a wide region of the design space with varied back-reflections, while still maintaining minimum feature sizes larger than 100 nm and even 130 nm. Using 3D-finite-difference time-domain simulations, we demonstrate devices with broadband low back-reflection of less than -20dB over more than 100 nm bandwidth centered around the C-band. Coupling efficiencies of 72% and 67% are achieved for minimum feature sizes of 106 nm and 130 nm, respectively. These gratings are also more fabrication tolerant compared to similar designs not using metamaterials.

On-chip Fourier-transform spectrometers and machine learning: a new route to smart photonic sensors.

Miniaturized silicon photonics spectrometers capable of detecting specific absorption features have great potential for mass market applications in medicine, environmental monitoring, and hazard detection. However, state-of-the-art silicon spectrometers are limited by fabrication imperfections and environmental conditions, especially temperature variations, since uncontrolled temperature drifts of only 0.1°C distort the retrieved spectrum precluding the detection and classification of the absorption features. Here we present a new strategy that exploits the robustness of machine learning algorithms to signal imperfections, enabling recognition of specific absorption features in a wide range of environmental conditions. We combine on-chip spatial heterodyne Fourier-transform spectrometers and supervised learning to classify different input spectra in the presence of fabrication errors, without temperature stabilization or monitoring. We experimentally show the differentiation of four different input spectra under an uncontrolled 10°C range of temperatures, about $ 100\times $100× increase in operational range, with a success rate up to 82.5% using state-of-the-art support vector machines and artificial neural networks.

Mapping the global design space of nanophotonic components using machine learning pattern recognition.

Nanophotonics finds ever broadening applications requiring complex components with many parameters to be simultaneously designed. Recent methodologies employing optimization algorithms commonly focus on a single performance objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. The behavior for multiple performance criteria is visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in modern nanophotonic design and provides a powerful tool to explore complexity in next-generation devices.