In this paper, we demonstrate the first attempt of encapsulating a flexible micro battery into a contact lens to implement an eye-tracker. The paper discusses how to scale the battery to power various circuits embedded in the contact lens, such as ASIC, photodiodes, etc., as well as how to combine the battery with external harvested energy sources. The fabricated ring battery has a surface area of 0.75 cm² yielding an areal capacity of 43 µAh·cm⁻² at 20C. Based on simulated 0.35-µm CMOS ASIC power consumption, this value is large enough to allow powering the ASIC for 3 minutes. The functioning of the micro battery is demonstrated by powering an orange LED.

Most eye trackers nowadays are video-based, which allows for a relatively simple and non-invasive approach but also imposes several constraints in terms of necessary computing power and conditions of use (e.g., lighting, spectacles, etc.). We introduce a new eye tracker using a scleral lens equipped with photodiodes and an eyewear with active illumination. The direction of gaze is obtained from the weighted average of photocurrents (centroid) and communicated through an optical link. After discussing the optimum photodiodes configuration (number, layout) and associated lighting (collimated, Lambertian), we present prototypes demonstrating the high performances possibilities (0.11° accuracy when placed on an artificial eye) and wireless optical communication.

Estimating in vivo the life span of a total knee replacement prosthesis is currently done by estimating the polyethylene (PE) wear rate from measurement of the femorotibial distance using X-ray photographies. This efficient method requires, however, waiting for few years to obtain a readout. This letter proposes using another metric that can be obtained within a couple of months of surgery, namely the center of pressure (COP). This metric represents the point, where the axial force applies the most onto the tibial tray. The displacement of the COP with respect to its ideal position can be used to estimate the wear and the life span of the PE. This requires the implant to be fitted with a telemetry system described briefly. The proposed method is supported by measures and simulations.

We present the design, fabrication, and test of a multipurpose integrated circuit (Application Specific Integrated Circuit) in AMS 0.35 µm Complementary Metal Oxide Semiconductor technology. This circuit is embedded in a scleral contact lens, combined with photodiodes enabling the gaze direction detection when illuminated and wirelessly powered by an eyewear. The gaze direction is determined by means of a centroid computation from the measured photocurrents. The ASIC is used simultaneously to detect specific eye blinking sequences to validate target designations, for instance. Experimental measurements and validation are performed on a scleral contact lens prototype integrating four infrared photodiodes, mounted on a mock-up eyeball, and combined with an artificial eyelid. The eye-tracker has an accuracy of 0.2°, i.e., 2.5 times better than current mobile video-based eye-trackers, and is robust with respect to process variations, operating time, and supply voltage. Variations of the computed gaze direction transmitted to the eyewear, when the eyelid moves, are detected and can be interpreted as commands based on blink duration or using blinks alternation on both eyes.

Clique-based neural networks implement low-complexity functions working with a reduced connectivity between neurons. Thus, they address very specific applications operating with a very low-energy budget. However, the implementation in the state of the art is not flexible and a fabricated circuit is only usable in a unique use case. Besides, the silicon area of hardwired circuits grows exponentially with the number of implemented neurons that is prohibitive for embedded applications. This paper proposes a flexible and iterative neural architecture capable of implementing multiple types of clique-based neural networks of up to 3968 neurons. The circuit has been integrated in an ST 65-nm CMOS ASIC and occupies a 0.21-mm² silicon surface area. The proper functioning of the circuit is illustrated using two application cases: a keyword recovery application and an electrocardiogram classification. The neurons outputs are updated 83 ns after a stimulation, and a neuron needs an energy of 115 fJ to propagate a change at the input to its output.

To operate under severe energy constraints, clique-based neural networks are good candidates. They benefit from a reduced exchange of information between low-complexity processing units with no performance degradation. This paper proposes a modular, flexible and scalable architecture validated by an ST 65-nm CMOS ASIC implementation for a 30-neuron clique-based neural network circuit. With 0.8V power supply, 150nA unitary current and a low performance degradation, the neuron energy consumption is reduced to only 7fJ per synaptic event. The network occupies a 41,820μm2 silicon area.