Inverse design of an on-chip optical response predictor enabled by a deep neural network.

We proposed inverse-designed nanophotonic waveguide devices which have the desired optical responses in the wide band of 1450-1650 nm. The proposed devices have an ultra-compact size of just 1.5 µm × 3.0 µm and are designed on a silicon-on-insulator (SOI) waveguide platform. Individual nano-pixels with dimensions of 150 nm × 150 nm were made of either silicon or silicon dioxide, and the materials for the 200 total cells were determined using a trained deep neural network. While training the two networks, the hyperparameter optimization method was applied to make the training process efficient. We then fabricated the proposed devices using a CMOS-compatible fabrication process, and experimentally verified the fabricated device performance.

Demonstration of high-accuracy 3D imaging using a Si optical phased array with a tunable radiator.

Precise imaging in three-dimension (3D) is an essential technique for solid-state light detection and ranging (LiDAR). Among various solid-state LiDAR technologies, silicon (Si) optical phased array (OPA)-based LiDAR has the significant advantage of robust 3D imaging due to its high scanning speed, low power consumption, and compactness. Numerous techniques employing a Si OPA have utilized two-dimensional arrays or wavelength tuning for longitudinal scanning but the operation of those systems is restricted by additional requirements. Here, we demonstrate high-accuracy 3D imaging using a Si OPA with a tunable radiator. As we adapted a time-of-flight approach for distance measurement, we have developed an optical pulse modulator that allows a ranging accuracy of less than 2 cm. The implemented Si OPA is composed of an input grating coupler, multimode interferometers, electro-optic p-i-n phase shifters, and thermo-optic n-i-n tunable radiators. With this system, it is possible to attain a wide beam steering range of 45° in a transversal angle with a 0.7° divergence angle, and 10° in a longitudinal angle with a 0.6° divergence angle can be achieved using Si OPA. The character toy model was successfully imaged in three dimensions with a range resolution of 2 cm using the Si OPA. The further improvement of each component of the Si OPA will allow even more accurate 3D imaging over a longer distance.

Demonstration of beam steering using a passive silica optical phased array with wavelength tuning.

We demonstrate beam steering using a passive silica optical phased array (OPA) with wavelength tuning. In this OPA, a constant path difference is built up to assign sequential phase delays with a wavelength variation in arrayed waveguide channels for the beam steering. From as-fabricated 1 × 101 passive silica OPA chips, we successfully achieved beam forming with a transversal divergence angle of 0.57° at a 1548.3-nm wavelength and also beam steering of 15.4° by wavelength tuning of 30.7 nm. Combining a cylindrical lens in front of the end-fire radiators, the longitudinal divergence angle could be reduced from 13.0° to 0.42°. The side-mode suppression ratio of the beam was 10.3 dB at the center position. Through simulation, we analyzed the effects of the phase errors on the beam quality, due to the effective index fluctuation of the waveguide channels, and provided an allowable error range to attain beam forming from the passive OPA.

Deep neural network-based phase calibration in integrated optical phased arrays.

Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming in integrated OPAs. Our methodology focuses on precise phase control, individually tailored to each of the 64 OPA channels, incorporating electro-optic phase shifters. To effectively handle the inherent complexity arising from the numerous voltage set combinations required for phase control across the 64 channels, we employ a tandem network architecture, further optimizing it through selective data sorting and hyperparameter tuning. To validate the effectiveness of the trained DNN model, we compared its performance with 20 reference beams obtained through the hill climbing algorithm. Despite an average intensity reduction of 0.84 dB in the peak values of the beams compared to the reference beams, our experimental results demonstrate substantial agreements between the DNN-predicted beams and the reference beams, accompanied by a slight decrease of 0.06 dB in the side-mode-suppression-ratio. These results underscore the practical effectiveness of the DNN model in OPA beamforming, highlighting its potential in scenarios that necessitate the intelligent and time-efficient calibration of multiple beams.