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We report a wireless energy harvesting and telemetry storage system in 180 nm CMOS technology, demonstrated in situ in rat carcass. The implantable device has dimensions 13 mm × 15 mm and stores 87.5 mJ, providing a self-powering time of 8.5 s transmitting through tissue. We utilize an all-solid-state flexible supercapacitor of breakdown voltage 0.8 V and capacitance 400 mF to harvest incoming wireless power, followed by a boost converter CMOS that drives an active wireless transmitter at 1.5 V at 2.4 GHz in the industrial, scientific, and medical (ISM) band. The DC/DC converter component and switching frequency selection were guided by genetic algorithm analysis and use digital feedback to control the pulse width modulation (PWM), which slowly modifies the duty cycle to control output voltage fluctuations. This implantable medical device system presents the roadmap for batteryless energy harvesting in vivo and in clinical environments, exhibiting the highest operating storage density of 450 μJ/mm2 reported to date. [ABSTRACT FROM AUTHOR]

As implantable devices gain more widespread use, medical device manufacturers are constantly looking for novel materials that increase and improve capability and functionality. The packaging needs to be biocompatible, but it is also highly desirable for it to be radio transparent to facilitate wireless telemetry and remote powering. Low temperature co-fired ceramics (LTCC) provide a viable solution that meets these desired specifications while also having characteristics that support ease of manufacturing such as the ability for molding, shaping, and even embedding components within the material. In this work, inductor coils used for wireless telemetry and powering are integrated into the walls of the LTCC-based package to maximize the size of the passive component while optimizing the miniaturization of the implant. The package designed and fabricated in this work consisted of inductors approximating 20 mH with a quality factor of 7.8 at 2 MHz. When compared to similar devices in the literature the LTCC inductor out performed these devices when a power-link figure of merit is used for comparison. [ABSTRACT FROM AUTHOR]

The article describes the theory involved in the design of an inductively powered system for use with miniature implantable neural recording devices. In addition, it provides the designer with a detailed guide to the design of such systems and discusses a specific real-world application of inductive powering.

Abstract: Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. [ABSTRACT FROM AUTHOR]