Periodic Reporting for period 1 - SmartGas (A Universal Acoustic MEMS Gas Sensor with Machine Learning)
Berichtszeitraum: 2021-05-01 bis 2023-04-30
This project aims to develop a universal gas sensor. The current gas sensors are developed individually due to current sensing technologies. This project proposes an easy to manufacture, low-cost gas sensor that can be used with any gas with proper calibration. Reducing the cost of the gas sensor would make the sensor available to the wider public. CO poisoning in homes could be reduced and every car could have an air quality sensor improving the society's quality of life. The main idea of the project is exciting the acoustic resonance of a cavity with an in-plane resonator, and also detecting the resonance with an in-plane resonator. The frequency and damping of the acoustic resonance are a function of the gas inside the cavity. The gas content can be extracted with an intelligent setup. The overall objectives of the project are listed as follows:
RO1: Design of the acoustic resonance system consisting of the MEMS resonator and cavity and modeling the coupling between them.
RO2: Fabrication of the sensors and cavities in the cleanroom, finding the proper bonding technology to bond the MEMS resonator and cavity.
RO3: Characterization of the fabricated gas sensors in the lab with the guidance of a pulmonologist and using machine learning.
RO4: Development and implementation of an Innovation Management Plan.
The training and networking activities are planned in-line with the proposed research. The overall objectives of the project have been achieved to a great extent. The project successfully demonstrated that two in-plane MEMS resonators can be coupled through a frequency matched acoustic cavity. The coupling is a direct function of the gas content and can be utilized as a universal gas sensor. The damping of the acoustic resonance was also measured and can improve the gas selectivity.
RO1: Design of the acoustic resonance system consisting of the MEMS resonator and cavity and modeling the coupling between them.
RO2: Fabrication of the sensors and cavities in the cleanroom, finding the proper bonding technology to bond the MEMS resonator and cavity.
RO3: Characterization of the fabricated gas sensors in the lab with the guidance of a pulmonologist and using machine learning.
RO4: Development and implementation of an Innovation Management Plan.
The training and networking activities are planned in-line with the proposed research. The overall objectives of the project have been achieved to a great extent. The project successfully demonstrated that two in-plane MEMS resonators can be coupled through a frequency matched acoustic cavity. The coupling is a direct function of the gas content and can be utilized as a universal gas sensor. The damping of the acoustic resonance was also measured and can improve the gas selectivity.
The project activities can be summarized with the following activities:
1. Design of the Acoustic Resonance System: An acoustic resonance system consisting of in-plane MEMS resonators and a cavity was designed. One resonator is employed to drive the cavity acoustic resonance ("drive"), and another resonator is used for sensing the acoustic resonance ("sense"). As one of the novelties, length tapered frequency tuning combs that can operate without any displacement limit were designed. The frequency tuning is crucial to to match the MEMS resonator frequencies to the cavity acoustic resonance. Multiple device variants at different frequencies were designed and taped out.
2. Fabrication of the Gas Sensors: The designed sensors were fabricated with a two-mask SOI-MEMS process in UNAM cleanroom, Bilkent University. The fabrication also served a major part in my technical training. I received training for the cleanroom devices, and successfully fabricated the devices. There has been delays in the fabrication due to equipment failures but I went through these obstacles.
3. Characterization of the Gas Sensors: The fabricated devices were paired with electronics on a PCB for characterization. The devices were tested with and without the acoustic cavity to demonstrate the proof of concept. The acoustic resonance is excited by the drive resonator and the acoustic response is detected by the sense resonator. The test setup was automated to record the sense response while systematically sweeping the drive and sense frequencies. Acoustic coupling was not detected without the acoustic cavity on the MEMS die. A strong sense response was observed with the acoustic cavity when the MEMS drive and sense resonator frequencies match the cavity acoustic frequency. The cavity frequency response showing the acoustic damping was also extracted through the systematic frequency sweeps. The project successfully demonstrated the proof of concept for acoustic gas sensing. Two in-plane MEMS resonators can be coupled through the acoustic resonance of the cavity, the coupling and the acoustic damping could be used as a gas sensor.
4. Training: I received both technical and non-technical training throughout the project. The technical training includes UNAM clean-room training for the device fabrication (Prof. Demir, host), lab equipment training for device characterization (Prof. Demir and Prof. Atalar), and machine learning algorithms training for gas identification in the future steps of the proposed concept (Prof. Koc). Non-technical training includes UNAM Nanocolloquium seminars and organizing graduate seminar series in the EE dept., both of these activities improved my networking skills.
5. Dissemination and Exploitation Activities: The cleanroom equipment failures delayed the overall project timeline and the results were obtained towards the end of the project. The results were presented in the Bilkent University Graduate Research Conference, I gave a seminar in Bilkent University Graduate Seminar Series, the project was announced to a large audience in social media by the University, and the project results were also posted on the project website. The project results we accepted for publication as an oral presentation in the IEEE TRANSDUCERS Conference which will be held in Kyoto, Japan on June 25-29, 2023. The final paper was written and submitted, and will be available in IEEE Xplore in July 2023.
1. Design of the Acoustic Resonance System: An acoustic resonance system consisting of in-plane MEMS resonators and a cavity was designed. One resonator is employed to drive the cavity acoustic resonance ("drive"), and another resonator is used for sensing the acoustic resonance ("sense"). As one of the novelties, length tapered frequency tuning combs that can operate without any displacement limit were designed. The frequency tuning is crucial to to match the MEMS resonator frequencies to the cavity acoustic resonance. Multiple device variants at different frequencies were designed and taped out.
2. Fabrication of the Gas Sensors: The designed sensors were fabricated with a two-mask SOI-MEMS process in UNAM cleanroom, Bilkent University. The fabrication also served a major part in my technical training. I received training for the cleanroom devices, and successfully fabricated the devices. There has been delays in the fabrication due to equipment failures but I went through these obstacles.
3. Characterization of the Gas Sensors: The fabricated devices were paired with electronics on a PCB for characterization. The devices were tested with and without the acoustic cavity to demonstrate the proof of concept. The acoustic resonance is excited by the drive resonator and the acoustic response is detected by the sense resonator. The test setup was automated to record the sense response while systematically sweeping the drive and sense frequencies. Acoustic coupling was not detected without the acoustic cavity on the MEMS die. A strong sense response was observed with the acoustic cavity when the MEMS drive and sense resonator frequencies match the cavity acoustic frequency. The cavity frequency response showing the acoustic damping was also extracted through the systematic frequency sweeps. The project successfully demonstrated the proof of concept for acoustic gas sensing. Two in-plane MEMS resonators can be coupled through the acoustic resonance of the cavity, the coupling and the acoustic damping could be used as a gas sensor.
4. Training: I received both technical and non-technical training throughout the project. The technical training includes UNAM clean-room training for the device fabrication (Prof. Demir, host), lab equipment training for device characterization (Prof. Demir and Prof. Atalar), and machine learning algorithms training for gas identification in the future steps of the proposed concept (Prof. Koc). Non-technical training includes UNAM Nanocolloquium seminars and organizing graduate seminar series in the EE dept., both of these activities improved my networking skills.
5. Dissemination and Exploitation Activities: The cleanroom equipment failures delayed the overall project timeline and the results were obtained towards the end of the project. The results were presented in the Bilkent University Graduate Research Conference, I gave a seminar in Bilkent University Graduate Seminar Series, the project was announced to a large audience in social media by the University, and the project results were also posted on the project website. The project results we accepted for publication as an oral presentation in the IEEE TRANSDUCERS Conference which will be held in Kyoto, Japan on June 25-29, 2023. The final paper was written and submitted, and will be available in IEEE Xplore in July 2023.
Current state of the art gas sensors can achieve high sensitivity but their ability to measure different gases is limited. The limitation stems from the sensing technologies which rely on some interaction (chemical or optical) with the gas. The main goal of the project was development of a material-independent gas sensing technology that is simple and hence low-cost. The project demonstrated a universal gas sensor built with standard MEMS fabrication processes. The proof of concept work shows the promise of acoustic gas sensing. Both the acoustic frequency and damping are newly introduced sensing concepts into the gas sensing field. When coupled with machine learning, multiple gases can be detected with low-cost sensors with a material independent method which is a major advantage over the state of the art. I will continue to work on the acoustic gas sensing after the project to advance the concept.
The proposed acoustic universal gas sensing concept could improve the safety and life quality of the society. Special and not-cheap CO sensors are employed at homes to prevent gas poisoning at homes. Similarly air quality sensors used in cars and indoors measure CO2 and share the same problems with the CO sensors. Unfortunately CO sensors do not exist in every home and car, the proposed concept could significantly reduce the cost and enter every home and car to improve the environmental safety and air quality.
The proposed acoustic universal gas sensing concept could improve the safety and life quality of the society. Special and not-cheap CO sensors are employed at homes to prevent gas poisoning at homes. Similarly air quality sensors used in cars and indoors measure CO2 and share the same problems with the CO sensors. Unfortunately CO sensors do not exist in every home and car, the proposed concept could significantly reduce the cost and enter every home and car to improve the environmental safety and air quality.