We demonstrate the thermo-optic properties of silicon-rich silicon nitride (SRN) films deposited using plasma-enhanced chemical vapor deposition (PECVD). Shifts in the spectral response of Mach-Zehnder Interferometers (MZIs) as a function of temperature were used to characterize the thermo-optic coefficients of silicon nitride films with varying silicon contents. A clear relation is demonstrated between the silicon content and the exhibited thermooptic coefficient in silicon nitride films, with the highest achievable coefficient being as high as (1.65±0.08) ×10 -4 K -1 . Furthermore, we realize an SRN Multi-Mode Interferometer (MMI) based thermo-optic switch with over 20 dB extinction ratio and total power consumption for two-port switching of 50 mW.
Convolutional neural networks have become an essential element of spatial deep learning systems. In the prevailing architecture, the convolution operation is performed with Fast Fourier Transforms (FFT) electronically in GPUs. The parallelism of GPUs provides an efficiency over CPUs, however both approaches being electronic are bound by the speed and power limits of the interconnect delay inside the circuits. Here we present a silicon photonics based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently. Our all-optical FFT is based on nested Mach-Zender Interferometers, directional couplers, and phase shifters, with backend electro-optic modulators for sampling. The FFT delay depends only on the propagation delay of the optical signal through the silicon photonics structures. Designing and analyzing the performance of a convolutional neural network deployed with our on-chip optical FFT, we find dramatic improvements by up to 10 4 when compared to state-of-the-art GPUs when exploring a compounded figure-of-merit given by power per convolution over area. At a high level, this performance is enabled by mapping the desired mathematical function, an FFT, synergistically onto hardware, in this case optical delay interferometers.
In this study, we demonstrate the DC Kerr effect in plasma-enhanced chemical vapor deposition silicon rich amorphous silicon carbide (a-SiC). Using the resonance shift of the transmission spectra of a ring resonator, we experimentally extract the third order nonlinear susceptibility χ3 to be 6.90×10−19 m2/V2, which is estimated to be more than six times higher than previous reported values in stoichiometric a-SiC. The corresponding induced second order nonlinear susceptibility χ2 of 44.9 pm/V is also three times higher than the reported value in silicon and silicon rich nitride utilizing the DC Kerr effect. The high nonlinearity makes silicon rich a-SiC a good materials candidate for nonlinear photonic applications.
We demonstrate the DC-Kerr effect in PECVD Silicon-rich Nitride (SRN) and use it to demonstrate a third order nonlinear susceptibility, 𝝌 (𝟑) , as high as (6 + 0.58)x10 -19 m 2 /v 2 . We employ spectral shift versus applied voltage measurements in a racetrack ring resonator as a tool by which to characterize the nonlinear susceptibilities of these films. In doing so we demonstrate a 𝝌 (𝟑) larger than that of silicon and argue that PECVD SRN can provide a versatile platform for employing optical phase-shifters while maintain a low thermal budget using a deposition technique readily available in CMOS process flows.
The design, fabrication, and characterization of low loss ultra-compact bends in high index (n = 3.1 at λ = 1550 nm) PECVD silicon rich silicon nitride (SRN) is demonstrated and utilized to realize efficient, small footprint thermo-optic phase shifter. Compact bends are structured into a folded waveguide geometry to form a rectangular spiral within an area of 65 x 65 µm 2 possessing a total active waveguide length of 1.2 mm. The device features a phase shifting efficiency of 8 mW/𝝅 and a 3 dB switching bandwidth of 15 KHz. We propose SRN as a promising thermo-optic platform combining the properties of silicon and silicon stoichiometric nitride.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.