There are many clinical scenarios in which continuous monitoring of vital physiological signs is desired. Pressure monitoring in particular has diverse uses in the body. Specific application areas include checking arterial blood pressure for detection of changing heart conditions, monitoring intracranial pressure for stroke victims, tracking post-surgery pressure profiles in spinal columns to determine treatment efficacy, and recording subtle changes in intraocular pressure to detect the onset of glaucoma [1, 2, 3, 4]. Many of these applications are in space-constrained regions of the body that are difficult to access. A sufficiently small implantable solution would address the challenges of obtaining accurate pressure measurements in such inaccessible regions of the body, and a wireless solution would address the challenge of continuous access. Such a device would promote effective intervention and treatment for chronic, pressure-dependent conditions.
For the continuous monitoring of chronic conditions, an implantable device should possess certain features: 1) Implantation should be minimally invasive, 2) its use and operation should not be an encumbrance to physician or patient, and 3) it should operate maintenance-free for several months to years. For minimally invasive procedures it is desirable to maintain an extremely small form factor, on the order of cubic millimeters in volume. An in vivo monitoring device should not have any external wires or “wearable” components; instead relying on wireless transmission for readout. Ensuring maintenance-free operation rules out the use of batteries and active elements, and necessitates a low drift sensor.
Based on the above criteria, passive wireless sensor technology is a natural platform for such a device. Passive wireless sensors are realizable in small form factor using micro-fabricated inductors in conjunction with MEMS capacitive sensors, thus allowing for minimally invasive implantation. This setup uses an inductive coil for wireless power transfer, thus eliminating any external leads. The combination of the inductor and variable capacitor circuit creates a resonant tank whose resonant frequency is dependent on the sensed value, thus creating a maintenance-free, battery-less sensing system. This device can be encased in a biocompatible material to ensure long-term operation.
Prior works have implemented this design paradigm to produce implantable wireless pressure sensors, but have yielded devices that are too large for certain clinical applications, such as spinal pressure monitoring [5, 6]. Implantable wireless sensors typically operate at resonance in the low frequency domain for efficient through-body power transfer [7], which necessitates the use of large inductance or capacitance values that require large areas. However, inductors with small areas have lower power transfer due to smaller flux, which reduces signal-to-noise ratio and makes read-out more difficultHence, an optimization is necessary to balance the needs of small size and low frequency while maximizing the deviceresponse to external signals for robust and reliable communication.
Unfortunately, most literature on LC tank design optimization is geared towards off-resonance filtering, or high-frequency power matching, which is unsuitable for low-frequency on-resonance use [8, 9]. While simulation software does exist to predict device performance in this regime, they tend to be laborious; optimizing device geometry requires an extremely long time [10, 11]. Fabrication and measurement of devices is possible and fairly simple, but this method has a long turn-around time and is costly. Thus there exists a need for fast analytic design and subsequent optimization of inductor geometry for use in low-frequency resonant tanks.
In this work, a quick turn-around design methodology is developed for planar inductors to be used in the low frequency magnetic coupling regime. A parameterized model is employed for planar inductive coils coupled with sensor capacitors. Designs are optimized with clinical, fabrication and operational constraints in mind. Calculations are compared tosimulations and measurements of such devices, and the models are refined to improve agreement.