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Related Experiment Video

Updated: May 29, 2025

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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Automatic Characterization of High-Performance MEMS-Based IR Sensors.

Matthew Benson1, Ryan W Parker1, Melisa Ekin Gülseren1

  • 1Department of Electrical and Computer Engineering, University of California at Davis, Davis, CA 95616, USA.

IEEE Access : Practical Innovations, Open Solutions
|February 7, 2025
PubMed
Summary

An automated system rapidly characterizes micro-electromechanical system (MEMS)-based infrared (IR) sensors, revealing performance trade-offs. This cost-effective platform enables faster development of next-generation IR sensing technologies.

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Area of Science:

  • Optoelectronics and Sensor Technology
  • Materials Science
  • Micro-electromechanical Systems (MEMS)

Background:

  • Infrared (IR) sensors are crucial for applications in spectroscopy, health monitoring, and communications.
  • Metasurface-enhanced micro-electromechanical system (MEMS)-based IR sensors show high performance, but experimental characterization is time-consuming and costly.
  • Determining the performance limits of novel IR sensing technologies is essential for their advancement.

Purpose of the Study:

  • To develop an automated, low-cost, and rapid solution for characterizing miniaturized IR sensing devices.
  • To experimentally determine the performance limits of MEMS-based IR sensors.
  • To identify trade-offs and statistical relationships in sensor performance based on device geometry.

Main Methods:

Keywords:
Automationcharacterizationinfraredmetasurfacesmicro-electromechanical systems (MEMS)radiofrequency (RF)sensors

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Last Updated: May 29, 2025

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  • An automated platform was designed for rapid characterization of on-chip IR sensors.
  • The system utilizes optical and radiofrequency interrogation techniques for efficient testing.
  • The platform can test over 1500 sensors on a single wafer, with approximately 20 seconds per device.

Main Results:

  • The automated system successfully characterized over 1500 MEMS-based IR sensors.
  • Data revealed significant trade-offs between responsivity and noise related to device thickness.
  • Statistical analysis provided insights into sensing response variations with device geometrical dimensions.
  • The best devices achieved a quality factor of 2391 and a responsivity of 164 Hz/nW.

Conclusions:

  • The proposed automated platform offers an efficient and cost-effective method for characterizing next-generation IR sensing devices.
  • This system accelerates the understanding and optimization of MEMS-based IR sensors.
  • The findings facilitate the development of advanced IR sensing technologies for diverse applications.