Yasha Karimi

Design of a backscatter-based Tag-to-Tag system

Practical technologies for the Internet of Things (IoT) must provide connectivity to all objects under a common framework irrespective of their size or value. Power requirement, cost of wireless devices and scalability have proved critical bottlenecks for the universal deployment of the IoT. One approach to address these issues is the use of a communication paradigm where the devices communicate via backscattering and exploit harvested power from an external RF source. In a Backscattering Tag-to-Tag Network (BTTN), the tags themselves are able to read and interpret the backscattered communications from other neighboring tags. In the tag-to-tag link, the BTTN tag has to demodulate a receiving signal with a low modulation index. In order to improve the link range, we propose a power-efficient demodulator design that enables the receiving tag to quantify the amplitude-shift keying (ASK) modulated signal with a modulation index as low as 0.6%. The demodulator consumes 1.21 µW at 1.1 V supply voltage at a data rate of 10 kbps.

Perspective Paper—Can AC Computing Be an Alternative for Wirelessly Powered IoT Devices?

An alternative computing paradigm is explored in this letter with application to wirelessly powered Internet of Things (IoT) devices. Contrary to existing methods that rely on DC computing, the wirelessly harvested AC power is directly used for computation by leveraging charge-recycling theory. The proposed approach has the potential to significantly reduce the energy cost, one of the primary barriers that slows down the global scalability of IoT devices. This opportunity and related challenges are investigated in this letter to provide guidelines for future research in the field.

Analog front end design for tags in backscatter-based tag-to-tag communication networks

Backscatter-based tag-to-tag communication (BBTT) is a paradigm wherein radio-less devices communicate with each other by using purely passive backscatter modulation. This allows for highly inexpensive and low power devices. Traditional backscattering devices like RFID tags are designed to communicate directly with an active reader leading to a centralized framework centered on the reader. Under a BBTT network, the tags talk to each other using backscattering in the presence of an external excitation signal, which can come from multiple sources (e.g., dedicated exciters, WiFi access points, TV towers, or cell phone towers). The two main components that determine the range and robustness of a passive tag-to-tag link are the power harvesting and demodulation circuit blocks in the analog front end (AFE). In this paper, we investigate the design constraints, optimization goals, and tradeoffs in the design of the AFE for BBTT tags. We first analyze the BBTT link theoretically and then verify the predicted optimal AFE parameters by simulations.

Energy efficient AC computing methodology for wirelessly powered IoT devices

Charge-recycling based AC computing has recently been proposed to significantly increase energy efficiency in wirelessly powered devices. The power consumption is reduced by 1) eliminating the rectification and regulation stages of traditional DC computing and 2) recycling charge through AC computing. An alternative charge-recycling mechanism is proposed in this paper that does not require a phase shifter or peak detector, thereby reducing the overhead power consumption. Simulation results in 45 nm technology demonstrate that an additional 60% reduction in power consumption can be achieved while operating at the same frequency. As compared to the traditional case, power consumption is reduced by more than an order of magnitude.

In-vivo tests of an inductively powered miniaturized neural stimulator

This work introduces the smallest wirelessly powered neural implant to date. We provide experiment verification by successfully stimulating the sciatic nerve of a rat. Power is deliverd over a 1.7 GHz inductive link at a distance of 0.5 cm. A method is also proposed to generate biphasic current pulses without the use of a controller. The entire system is fabricated in a 0.13 μm CMOS process and occupies merely 180 μm × 180 μm.

Live demonstration: A wirelessly powered highly miniaturized neural stimulator

The smallest wirelessly powered neural implant to date is demonstrated. Power is sent over a near-field inductive link. The implant system is realized on a single CMOS ASIC which includes the on-chip coil, the harvesting circuit, and the current driver. The entire system is fabricated in a 0.13 μm CMOS process and occupies merely 180 μm × 180 μm.

No nanosensor and single exhale breathalyzer for asthma monitoring

This work describes the development of a nanotechnology solution for the non-invasive detection and monitoring of diseases, such as asthma, by identifying and measuring the concentration of nitric oxide (NO) in exhaled breath. Nitric oxide is a known breath biomarker and the medical literature has published, recently, guidelines correlating the amounts of NO in breath with various airway diseases. A scalable process (flame spray pyrolysis) was employed to form nanoparticles of the NO-gas selective □-WO3. A wireless prototype of a portable, handheld breathalyzer utilizing resistive sensors based on these nanomaterials was also designed. Single exhale measurements of NO were obtained with this breathalyzer prototype. This technology is expected to become an inexpensive and affordable personalized medical diagnostic tool in the near future.