To counter the exponential growth of computing power requirements for machine learning, efficient sailing of the integrated photonic processors has become a fundamental issue to be addressed. However, it remains challenging to properly calibrate the circuit imperfections, such as fabrication errors and crosstalk originating from both thermal and electric effects, which drastically affects the performance as the circuit size becomes larger. We demonstrate a silicon-photonics 16×16 Clements-type photonic vector-matrix multiplier. The degradation of fidelity caused by crosstalk and fabrication error was successfully compensated using our proposed machine learning based tuning method and deterministic calibration. The first experimental 10-digit MNIST classification was performed, which defines the classification results directly corresponding to the optical output ports. Furthermore, we also fabricated an 8 8 MZI-mesh photonic processor based on the planar lightwave circuit (PLC) technique which can realize low wavelength dependence operation due to low fabrication errors. This structure achieves the efficient throughput due to the O(N2W) operation, where N and W denote the number of spatial and wavelength channels, respectively. A high fidelity of 0.99 at 1550 nm and >0.96 over the C band was achieved, demonstrating the feasibility of the matrix-matrix multiplication operation with a combination of MZI-mesh and WDM.
KEYWORDS: Analog electronics, Silicon photonics, Nanophotonics, Calibration, Phase shifts, Matrices, Machine learning, Signal to noise ratio, Databases, Data modeling
We show our recent progress on a Clements-type16x16 on-chip matrix processor based on silicon photonics and a new type of electro-optic digital-to-analog converters (EO DACs) with a higher signal-to-noise ratio. For the former, we developed a machine-learning-based calibration technique that involves theoretical modeling with circuit parameters (loss, phase error, splitting ratio, and crosstalks), which is adequate to obtain better fidelity for large-scale imperfect interferometers. After the calibration, we demonstrated a 16x16 identity matrix and several permutation matrices with a high signal-to-noise ratio and a well-known MNIST database classification task. For the latter, we developed low-loss and wavelength insensitive EO DACs consisting of 1:1 Y splitters and phase modulators that are useful for DAC-less input units for photonic accelerators.
We report and elaborate the design of a hexapole mode of an H1 point-defect photonic crystal nanocavity with a theoretical quality (Q) factor over 108. Thanks to the C6 symmetry of the mode, our design uses only four structural modulation parameters, unlike that for many other ultrahigh-Q nanocavities based on complicated optimizations. Silicon (Si) planar H1 nanocavity samples prepared with the design exhibit a systematic variation in their resonant wavelengths by the spatial shift of air holes in 1 nm unit. Their maximum loaded Q factor is measured as 1.2 million, and the corresponding cavity’s intrinsic Q factor is estimated as 1.5 million.
In this talk, we theoretically and experimentally investigate intriguing optical properties of non-Hermitian coupled nanoresonators based on two dimensional photonic crystals. Firstly, we demonstrate that a one dimensional array of PT symmetric coupled nanoresonators exhibits exotic optical dispersion near the exceptional point (EP). Second, we demonstrate that a similar non-Hermitian coupled resonators having equal coupling strength exhibits a topological insulating phase when we appropriately pump specific resonators. This system is unique because we can create the topological insulating phase from a homogeneous resonator chain only by manipulating gain and loss with a certain order, leading to reconfigurable optical non-trivial topology. Thirdly, we show our recent experimental demonstration of the PT phase transition in non-Hermitian coupled resonators based on electrically pumped photonic crystal nanolasers. The result shows an interesting enhancement in the vicinity of EP.
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