Multi-layer inverse design of vertical grating couplers for high-density, commercial foundry interconnects.

Density-based topology optimization is used to design large-scale, multi-layer grating couplers that comply with commercial foundry fabrication constraints while simultaneously providing beam profiles that efficiently couple to a single-mode optical fiber without additional optics. Specifically, we describe the design process and experimentally demonstrate both single- and dual-polarization grating couplers that couple at normal incidence (0° from the normal) with low backreflections (-13.7 dB and -15.4 dB at the center wavelength), broad 3 dB bandwidths (75 nm and 89 nm), and standard coupling efficiencies (-4.7 dB and -7.0 dB). The dual-polarization grating couplers exhibit over 30 dB of polarization extinction across the entire band. The devices were fabricated on the GlobalFoundries 45CLO CMOS platform and characterized across three separate wafers. This new design approach produces distinct features for multiple foundry layers and yields emitters with arbitrary, user-specified far-field profiles.

High-performance hybrid time/frequency-domain topology optimization for large-scale photonics inverse design.

We present a photonics topology optimization (TO) package capable of addressing a wide range of practical photonics design problems, incorporating robustness and manufacturing constraints, which can scale to large devices and massive parallelism. We employ a hybrid algorithm that builds on a mature time-domain (FDTD) package Meep to simultaneously solve multiple frequency-domain TO problems over a broad bandwidth. This time/frequency-domain approach is enhanced by new filter-design sources for the gradient calculation and new material-interpolation methods for optimizing dispersive media, as well as by multiple forms of computational parallelism. The package is available as free/open-source software with extensive tutorials and multi-platform support.

Constellation-based identification of linear and nonlinear OSNR using machine learning: a study of link-agnostic performance.

We demonstrate accurate estimation of generalized optical signal to noise ratio (GOSNR) for wavelength division multiplexed fiber communication systems using an experimentally trained multi-tasking convolutional neural network while simultaneously estimating linear and nonlinear noise contributions. Using dual-polarized 32-GBaud 16QAM DWDM links we extract learnable features from constellation density matrices and accurately estimate GOSNR while simultaneously estimating linear and nonlinear contributions. Estimation of the OSNRASE, OSNRNL and GOSNR are demonstrated with < 0.5 dB mean absolute error. We also assess the universality of our model within the regime of metro networks by cross-training with data from such links comprised of different fiber types. We demonstrate a path to a practical universal training method that includes additional link parameters. The methods do not require contiguous high-speed sampling, additional hardware nor transmission of special symbols or patterns and are readily implemented in deployed systems.

Photonic topology optimization with semiconductor-foundry design-rule constraints.

We present a unified density-based topology-optimization framework that yields integrated photonic designs optimized for manufacturing constraints including all those of commercial semiconductor foundries. We introduce a new method to impose minimum-area and minimum-enclosed-area constraints, and simultaneously adapt previous techniques for minimum linewidth, linespacing, and curvature, all of which are implemented without any additional re-parameterizations. Furthermore, we show how differentiable morphological transforms can be used to produce devices that are robust to over/under-etching while also satisfying manufacturing constraints. We demonstrate our methodology by designing three broadband silicon-photonics devices for nine different foundry-constraint combinations.

Optical performance monitoring using digital coherent receivers and convolutional neural networks.

We experimentally demonstrate accurate modulation format identification, optical signal to noise ratio (OSNR) estimation, and bit error ratio (BER) estimation of optical signals for wavelength division multiplexed optical communication systems using convolutional neural networks (CNN). We assess the benefits and challenges of extracting information at two distinct points within the demodulation process: immediately after timing recovery and immediately prior to symbol unmapping. For the former, we use 3D Stokes-space based signal representations. For the latter, we use conventional I-Q constellation images created using demodulated symbols. We demonstrate these methods on simulated and experimental dual-polarized waveforms for 32-GBaud QPSK, 8QAM, 16QAM, and 32QAM. Our results show that CNN extracts distinct and learnable features at both the early stage of demodulation where the information can be used to optimize subsequent stages and near the end of demodulation where the constellation images are readily available. Modulation format identification is demonstrated with >99.8% accuracy, OSNR estimation with <0.5 dB average discrepancy and BER estimation with percentage error of <25%.

Advanced signaling technologies for high-speed digital fiber-optic links.

We summarize the most recent research of the Georgia Tech Terabit Optical Networking Consortium and the state-of-the-art in fiber telecommunications. These results comprise high-capacity single-mode fiber systems with digital coherent receivers and shorter-reach multimode fiber links with vertical cavity surface emitting lasers. We strongly emphasize the capabilities that sophisticated digital signal processing and electronics add to these fiber-based data transport links.

Joint digital signal processing for superchannel coherent optical communication systems.

Ultra-high-speed optical communication systems which can support ≥ 1Tb/s per channel transmission will soon be required to meet the increasing capacity demand. However, 1Tb/s over a single carrier requires either or both a high-level modulation format (i.e. 1024QAM) and a high baud rate. Alternatively, grouping a number of tightly spaced "sub-carriers" to form a terabit superchannel increases channel capacity while minimizing the need for high-level modulation formats and high baud rate, which may allow existing formats, baud rate and components to be exploited. In ideal Nyquist-WDM superchannel systems, optical subcarriers with rectangular spectra are tightly packed at a channel spacing equal to the baud rate, thus achieving the Nyquist bandwidth limit. However, in practical Nyquist-WDM systems, precise electrical or optical control of channel spectra is required to avoid strong inter-channel interference (ICI). Here, we propose and demonstrate a new "super receiver" architecture for practical Nyquist-WDM systems, which jointly detects and demodulates multiple channels simultaneously and mitigates the penalties associated with the limitations of generating ideal Nyquist-WDM spectra. Our receiver-side solution relaxes the filter requirements imposed on the transmitter. Two joint DSP algorithms are developed for linear ICI cancellation and joint carrier-phase recovery. Improved system performance is observed with both experimental and simulation data. Performance analysis under different system configurations is conducted to demonstrate the feasibility and robustness of the proposed joint DSP algorithms.

Efficient photoconductive terahertz source using line excitation.

We report significant improvement in terahertz (THz) power and efficiency using photoconductive sources by use of a spatially extended line source excitation and the trap enhanced field effect that occurs in sources made on semi-insulating GaAs. The combination of high electric fields and reduced screening effects allows 10 microW of THz power to be generated with 14 mW of absorbed optical power, demonstrating nearly 0.1% optical-to-THz conversion efficiency.