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Si-Waveguide Absorption-based Methane Gas Sensor PIC
Tools Used: OptSim Circuit
Overview
Due to the recent advances in materials, nanostructures, silicon technologies, modeling tools, process design kits (PDKs), and manufacturing infrastructure, the photonic integrated circuits (PIC) are becoming increasingly widespread. The application area for the PICs is also becoming much wider ranging from data center interests such as transceivers and switches to more diverse automotive, biomedical and sensing markets such as LiDAR, tomography and free-space sensors. Synopsys PIC Design Suite [1] facilitate a compete PIC design for manufacturing (DFM) flow from ideas to tape out, including schematic entry, electronics-photonics co-simulation, run-to-run/wafer-to-wafer process variations, layout, back-annotation, verification signoffs and GDS II handoff.
The Simulation Setup
In this application case study, we demonstrate modeling of a silicon photonic sensor chip design for detecting Methane based on the design, parameters and analyses presented in [2]. Those interested in learning more about advances in silicon waveguide spectroscopy for sensing applications are encouraged to explore [3-7]. The schematic is shown in Figure 1.
Figure 1. Schematic of the Methane gas sensor PIC.
The topology of the silicon photonic waveguide absorption spectrometer comprises of the following OptSim Circuit models and design parameters:
A continuous wave (CW) laser launches light into the sensor PIC comprising of input and output couplers, and the Si waveguide. The receiver is configured with a responsivity of 1.0 and its current noise density is defined as a variable calculated from the desired noise equivalent absorption (NEA) [8]. This calculation produces a 100-ppmv detection limit with 60-second averaging time for the sensor chip as designed.
Simulation and Results
The plot in Figure 2 shows noisy simulated concentration in parts per million in volume (Cppmv) measurements for actual concentrations of 0, 500, and 1000.
Figure 2. Methane concentration in parts-per-million by volume (ppmv).
Allan Deviation Analysis
Allan deviation plots are commonly used to analyze long-term stability of sensors [9]. The Allan deviation is square-root of the variance. The highlighted blocks as shown in Figure 3 produce electrical signal whose average power is the Allan variance in ppmv2.
Figure 3. Schematic of the Methane gas sensor PIC with Allan variance calculations.
The plot in Figure 4 shows noisy simulated Cppmv measurements for actual Cppmv concentrations of 0, 500, and 1000.
Figure 4. Allan deviation plot for the Methane sensor PIC under the current study as a function of launch power.
The Allan deviation (ppmv) versus averaging time matches results from [2] for 10-dBm launch power. The Detection limit improves for higher launch power which is consistent with assumption that sensor noise is independent of signal power.
Similarly, we can plot Allan deviation versus waveguide length or intrinsic waveguide loss as shown in Figure 5.
Figure 5. Allan deviation plot for the Methane sensor PIC under the current study as functions of waveguide length and loss.
As can be seen, there is a minimum detection limit for a ~2.2-cm waveguide length (1/alpha_wg), while the detection limit improves as waveguide loss decreases. The detection limit is optimum at waveguide length of 1/alpha_wg [7]. These results assume that the sensor noise is independent of signal level.
The application note demonstrated modeling of a Methane gas sensor PIC in OptSim Circuit. We also carried out the Allan deviation analysis. The simulation results match with the references cited. For more information and to request a demo, please contact photonics_support@synopsys.com.
Authors
Dr. Pablo V. Mena, R&D Engineer, Sr. Staff
Jigesh K. Patel, Product Manager
References
1. https://www.synopsys.com/photonic-solutions/pic-design-suite.html
2. L. Tombez, E. J. Zhang, J. S. Orcutt, S. Kamlapurkar, and W. M. J. Green, “Methane absorption spectroscopy on a silicon photonic chip,” Optica, vol. 4, no. 11, Nov. 2017, pg. 1322-1325
3. Arthur Nitkowsi, Antje Baeumner, and Michal Lipson, “On-chip spectrophotometry for bioanalysis using microring resonators,” Biomedical Optics Express, vol. 2, no. 2, Feb. 2011, pg. 271-277
4. Z. Han, et al., “On-chip mid-infrared gas detection using chalcogenide glass waveguide,” Applied Physics Letters, vol. 108, DOI 10.1063/1.4945667, 2016
5. Z. Han, et al., “On-chip chalcogenide glass waveguide-integrated mid-infrared PbTe detectors,” Applied Physics Letters, vol. 109, DOI 10.1063/1.4961532, 2016
6. Jane Hodgkinson, and Ralph P. Tatam, “Optical gas sensing: a review,” Measurement Science and Technology, vol. 24, pg. 1-59, 2013
7. Hongtao Lin, et al., “Mid-infrared integrated photonics on silicon: a perspective,” Nanophotonics, vol. 7, no. 2, 2018, pg. 292-420
8. https://www.thorlabs.com/images/TabImages/Noise_Equivalent_Power_White_Paper.pdf
9. Marilena Giglio, et al., “Allan deviation plot as a tool for quartz-enhanced photoacoustic sensors noise analysis,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 63, no. 4, April 2016, pg. 555-560