Related Experiment Video
Updated: May 9, 2026
08:25
Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
Published on: August 27, 2021
A photovoltaic-driven and energy-autonomous CMOS implantable sensor
Sahar Ayazian1, Vahid A Akhavan, Eric Soenen
1Electrical and Computer Engineering Department, The University of Texas at Austin, Austin, TX 78712 USA. ayaz@mail.utexas.edu
IEEE Transactions on Biomedical Circuits and Systems
|July 16, 2013
Summary
This study introduces an energy-autonomous, MRI-compatible implantable sensor powered by photovoltaic cells. The device measures physiological signals and transmits data wirelessly using frequency-shift keying modulation.
Area of Science:
- Biomedical Engineering
- Materials Science
- Electrical Engineering
Background:
- Developing implantable sensors requires energy autonomy and MRI compatibility.
- Existing sensors often face limitations in power supply and magnetic resonance imaging interference.
Purpose of the Study:
- To present an energy-autonomous, MRI-compatible CMOS implantable sensor.
- To demonstrate wireless physiological signal acquisition and transmission for in-vivo monitoring.
Main Methods:
- Utilized on-chip P+/N-well diode arrays as photovoltaic (PV) cells for power harvesting.
- Employed a subthreshold ring oscillator-based sensor for in-vivo physiological signal measurement.
- Implemented frequency-shift keying (FSK) modulation and neuromorphic transmission via polarized electrodes.
Main Results:
- Achieved energy autonomy using tissue-penetrating light for PV power generation.
- Developed a compact 2.5 mm × 2.5 mm integrated system operating at sub-μW power levels.
- Successfully transmitted acquired physiological data wirelessly to the skin surface.
Conclusions:
- The presented sensor offers a novel solution for long-term, untethered in-vivo physiological monitoring.
- The integration of PV cells and CMOS technology enables MRI-compatible, energy-autonomous sensing.
- This technology has potential applications in advanced neuromorphic and biomedical monitoring systems.
