Wirelessly powered implants are increasingly being developed to interface with neurons in the brain. They often rely on microelectrode arrays which are limited by their scalability and long-term stability because of their physical size and rigid configuration. Yet some clinical and research applications prioritize a distributed neural interface over one that offers high resolution. One solution to make large scale, fully specifiable, electrical stimulation/recording possible, is to disconnect the electrodes from the base, so that they can be arbitrarily placed, using a syringe, freely in the nervous system. In this work, a wirelessly powered stimulating implant is miniaturized using a novel electrode integration technique, and its implanted depth maximized using innovative optimization design methods for the transmitter and receiver coils. The stimulating device is implemented in a 130nm CMOS technology with the following characteristics: 300 μm × 300 μm × 80 μm size; optimized wireless powering; and integrated circuit, electrodes and coil. The wireless and stimulation capability of the implant is validated in a conductive medium and in-vivo. To the best of our knowledge, the fabricated free-floating miniaturized implant has the best depth-to-volume ratio making it an excellent tool for minimally-invasive scalable neural interface, and thus could eventually replace the rigid arrays that are currently the state-of-the-art in brain set-ups.

RF-powered backscatter communication between passive tags holds tremendous potential as an enabling technology for a ubiquitous ‘Internet of Things’ (IoT). We develop a Backscattering Tag-to-Tag Network (BTTN), comprised of passive tags capable of large-scale, passive, multi-hop communication with each other via backscatter modulation of an external RF excitation signal. The low sensitivity and lack of active demodulator on passive tags present significant challenges to the communication, including a unique phase cancellation problem, which significantly affects the range and robustness of a passive tag-to-tag link. We overcome these challenges using innovative tag architecture and also develop a novel multi-phase backscatter modulation technique with a learning mechanism that overcomes the phase cancellation problem. This improves the link performance bringing passive tag-to-tag communication closer to practical use. The additional hardware compared to the conventional RFID tag architecture includes one more terminating impedance in the modulator. The data rate is reduced due to backscatter at two different phases while the increase in the power consumption is negligible. We develop prototype BTTN tag hardware and firmware and evaluate its performance. The prototype achieves link ranges of up to 3m at 5 Kbps with an excitation power level of only –20dBm while successfully overcoming phase cancellation. We further extend BTTN operation to a multihop network where we demonstrate a 4 hop link capable of communicating over 12m under similar conditions.

A common feature of the inflammatory response in patients who have actually contracted influenza is the generation of a number of volatile products of the alveolar and airway epithelium. These products include a number of volatile organic compounds (VOCs) and nitric oxide (NO). These may be used as biomarkers to detect the disease. A portable 3-sensor array microsystem-based tool that can potentially detect flu infection biomarkers is described here. Whether used in connection with in-vitro cell culture studies or as a single exhale breathalyzer, this device may be used to provide a rapid and non-invasive screening method for flu and other virus-based epidemics.

There has been a significant increase in the number of total knee replacement (TKR) surgeries over the past few years, particularly among active young and elderly people suffering from knee pain. Continuous and optimal monitoring of the load on the knee is highly desirable for designing more reliable knee implants. This paper focuses on designing a smart knee implant consisting of a triboelectric energy harvester and a frontend electronic system to process the harvested signal for monitoring the knee load. The harvester produces an AC signal with peak voltages ranging from 10 V to 150 V at different values of knee cyclic loads. This paper demonstrates the measurement results of a PCB prototype of the frontend electronic system fabricated to verify the functionality and feasibility of the proposed approach for a small range of cycling load. The frontend electronic system consists of a voltage processing unit to attenuate high peak voltages, a rectifier and a regulator to convert the input AC signal into a stabilized DC signal. The DC voltage signal provides biasing for the delta-sigma analog-to-digital converter (ADC). Thus, the output of the triboelectric harvester acts as both the power signal that is rectified/regulated and data signal that is digitized. The power consumption of the proposed PCB design is approximately $5.35~\mu \text{W}$ . Next, the frontend sensor circuitry is improved to accommodate a wider range of cyclic load. These results demonstrate that triboelectric energy harvesting is a promising technique for self-monitoring the load inside knee implants.

In this paper, an alternating current (ac) computing methodology is proposed for integration into wirelessly powered devices, such as radio-frequency (RF) tags and sensor nodes. Contrary to the traditional platforms that integrate direct current (dc)-powered computational logic along with the rectification and regulation stages, in the proposed approach, the harvested RF signal is directly used to power the data processing circuitry by leveraging the charge-recycling and adiabatic circuit theory. A near-field-based wireless power harvesting system with an 8-bit arithmetic logic unit is developed to evaluate the proposed framework. Simulation results in 45-nm technology demonstrate that the overall power consumption can be reduced by up to 16 times as compared to the conventional approach that relies on ac-to-dc conversion and static CMOS logic. This reduction in power enables significant computation capability for the RF-powered devices. Several important characteristics, such as the impact of circuit size on overhead and processing power, impact of voltage scaling on circuit operation, and power consumption, are investigated. Some important design issues and related tradeoffs are also discussed.

An analysis flow is proposed to determine the significance of induced (switching) noise in analog circuits. The proposed flow is exemplified through two commonly used amplifier topologies. Specifically, input-referred switching noise is introduced as the first figure-of-merit and compared with the well-known equivalent input device noise through analytic expressions. The comparison is achieved as a function of multiple parameters that characterize switching noise in the time domain (modeled as a decaying sine wave), such as peak amplitude, period, oscillation frequency within each period, and damping coefficient. The analytic expressions used to obtain input-referred switching and device noise are verified with SPICE simulations. These expressions are utilized to develop dominance regions for both noise sources. Furthermore, time-domain switching noise amplitude (at the bulk node) at which the input device and switching noise magnitude are equal (in the frequency domain) is determined as the second figure-of-merit, providing guidelines for the signal isolation process. Reverse body biasing is also proposed to alleviate the effect of switching noise by weakening the bulk-to-input transfer function as opposed to reducing the switching noise amplitude at the bulk nodes. It is demonstrated that this method has a negligible effect on primary design objectives of the victim circuit while reducing the input-referred switching noise by up to 10 dB. As a case study, the proposed flow is applied to a potentiostat circuitry where input sensitivity is of primary importance.