Accelerating silicon photonic parameter extraction using artificial neural networks

Abstract

We present a novel silicon photonic parameter extraction tool that uses artificial neural networks. While other parameter extraction methods are restricted to relatively simple devices whose responses are easily modeled by analytic transfer functions, this method is capable of extracting parameters for any device with a discrete number of design parameters. To validate the method, we design and fabricate integrated chirped Bragg gratings. We then estimate the actual device parameters by iteratively fitting the simultaneously measured group delay and reflection profiles to the artificial neural network output. The method is fast, accurate, and capable of modeling the complicated chirping and index contrast.

The Intelligent Design of Silicon Photonic Devices

“…[ 150 ] Figure 20c [ 151 ] and d [ 152 ] present another example of developing a grating coupler with deep learning, including forward modeling and inverse design. Lately, the FCN has extended to a wider range of communication‐relevant photonic platforms, including Bragg grating, [ 153 ] directional coupler, [ 154 ] nanophotonic waveguide, [ 155 ] and power splitter, [ 156 ] as sketched in Figure 20e–h.…”

“…Copyright 2021, IEEE, Copyright 2019, IEEE; e) Bragg grating. Reproduced with permission [153]. Copyright 2019, Optical Society of America; f) Directional coupler.…”

Deep learning: a new tool for photonic nanostructure design

“…Nanostructures and metadevices are beginning to play an important role in integrated photonics 71 besides the fact that silicon photonic devices 72 typically also contain features with sub-micron dimensions. 73,74 The use of nanoscale features in silicon photonics introduces a vulnerability to fabrication related variations and defects which need to be well quantified. Several recent reports in the literature have focused on the application of DL to design problems in integrated photonics.…”

“…Hammond and co-workers 73 proposed a new parameter extraction method using DL and demonstrated its applicability in extracting the true physical parameters of a fabricated Chirped Bragg Grating (CBG). Gostimirovic and co-workers 76 reported the use of DL in the accelerated design of polarization-insensitive subwavelength grating (SWG) couplers on a SOI (silicon-on-insulator) platform.…”

A General Neural Network Model for Complex Refractive Index Extraction of Low-Loss Materials in the Transmission-Mode THz-TDS

“…The artificial neural network finds initial applications in extracting both the refractive index and thickness of single-layer optical thin films in the visible region [ 19 , 20 , 21 ]. With the improvement of computer performance and the rise of many new disciplines and technologies, the use of neural network method in material characterization extends to quasi-crystalline alloy (Al 80 Mn 20 ) [ 22 ], silicon photonics [ 23 ], 2D materials (MoSe 2 , WS 2 , WSe 2 ) [ 24 ], 3D nanonetwork silicon structures [ 25 ], and binary ionic liquid system [ 26 , 27 ]. Recently, a new standard was proposed to evaluate the reliability of the optical parameter measurement of thin films via the neural network method [ 28 ].…”