An integrated electro-optic modulator based on a waveguide Bragg grating in thin-film lithium niobate is introduced. The compact device with a footprint of 10 × 400 μm2 displays an optical extinction ratio of 53.8 dB at a wavelength of 1555.9 nm in the C-band. With a simple intensity modulation and direct detection scheme, data rates up to 100 Gbit/s are demonstrated with 2-, 4- and 8-level pulse-amplitude modulation formats. The optical filter characteristic of a waveguide Bragg grating modulator suppresses one optical sideband, resulting in inherent single sideband modulation. This enables transmission of a 100 Gbit/s on-off keying signal over 12 km of standard single-mode fiber at 1559.05 nm, without the need for chromatic dispersion compensation.

Bragg reflectors (BRFs) are essential elements in optical telecommunication and sensing applications. Their miniaturization down to the sub-micron scale has been achieved in silicon-on-insulator chips. However, their tunability is limited only to thermal tuning. In order to achieve a faster and more practical tunability operation, here we report on electro-optically tunable BRFs with $\sim 14\mathrm{dB}$ signal filtering on a lithium-niobate-on-insulator platform, while keeping sub-micron cross-sections. Due to the lithium niobate electro-optic properties and the chosen electrodes configuration, a Bragg tunability coefficient of $23.37\pm 0.55\mathrm{pm}/\mathrm{V}$ is achieved, which enhances $\sim 33$ times the tunability performance of state-of-the-art BRFs.

Miniaturised optical spectrometers are attractive due to their small footprint, low weight, robustness and stability even in harsh environments such as space or industrial facilities. We report on a stationary-wave integrated Fourier-transform spectrometer featuring a measured optical bandwidth of 325 nm and a theoretical spectral resolution of 1.2 nm. We fabricate and test on lithium niobate-on-insulator to take full advantage of the platform, namely electro-optic modulation, broad transparency range and the low optical loss achieved thanks to matured fabrication techniques. We use the electro-optic effect and develop innovative layouts to overcome the undersampling limitations and improve the spectral resolution, thus providing a framework to enhance the performance of all devices sharing the same working principle. With our work, we add another important element to the portfolio of integrated lithium-niobate optical devices as our spectrometer can be combined with multiple other building blocks to realise functional, monolithic and compact photonic integrated circuits.

Modern machine learning models use an ever-increasing number of parameters to train (175 × 109 parameters for GPT-3) with large datasets to achieve better performance. Optical computing has been rediscovered as a potential solution for large-scale data processing, taking advantage of linear optical accelerators that perform operations at lower power consumption. However, to achieve efficient computing with light, it remains a challenge to create and control nonlinearity optically rather than electronically. In this study, a reservoir computing approach (RC) is investigated using a 14-mm waveguide in LiNbO3 on an insulator as an optical processor to validate the benefit of optical nonlinearity. Data are encoded on the spectrum of a femtosecond pulse, which is launched into the waveguide. The output of the waveguide is a nonlinear transform of the input, enabled by optical nonlinearities. We show experimentally that a simple digital linear classifier using the output spectrum of the waveguide increases the classification accuracy of several databases by ∼10% compared to untransformed data. In comparison, a digital neural network (NN) with tens of thousands of parameters was required to achieve similar accuracy. With the ability to reduce the number of parameters by a factor of at least 20, an integrated optical RC approach can attain a performance on a par with a digital NN.