A backscatter tag-to-tag network enables battery-less communication by harvesting energy and reflecting wireless signals between tags, making it ideal for energy-efficient IoT applications such as asset tracking, structural health monitoring, and environmental sensing. Accurate localization is crucial for these applications. While RSSI-based (Received Signal Strength Indicator) localization is the most common method for RF localization—estimating distance based on the received signal strength—it is often dependent on the position and power of the excitation source. We present a novel distance estimation method based on the estimation of the channel path loss and phase between tags, which is independent of the excitation source’s position and power. The experimental results demonstrate millimeter-level accuracy in 67% of cases and 99% accuracy within 17 cm for tag-to-tag distances up to 2.4 meters at 915 MHz.

In the past three decades, we have witnessed unprecedented progress in wireless implantable medical devices (IMDs) that can interface with the nervous system. To provide an even more stable, safe, and distributed interface, a new class of implantable devices is being developed; single-channel sub-mm scale wireless devices. In this research, we describe a new and simple technique for the fabrication and assembly of a sub-mm wirelessly powered stimulating implant. The fabricated implant is composed of an ASIC that measures$\mathbf{900} \times \mathbf{450}\times \mathbf{80}\ \boldsymbol{\mu} \mathbf{m}^{\mathbf{3}}$, two PEDOT-coated microelectrodes, an SMD inductor, and a SU-8 coating. The electrodes and the SMD are directly mounted onto the ASIC. The ultra-small device is powered via electromagnetic (EM) in near-field using a 2-coil inductive link and shows a power transfer efficiency (PTE) of 0.17% in the air with coil separation of 0.5 cm.

Large scale networks of intelligent sensors that can function without any batteries will have enormous implications in applications that range from smart spaces to structural and environmental monitoring. RF tags present an amenable platform for sensor integration as the backscatter communication offers low energy cost of communication. Current RF tags either use extremely low-power sensors or perform tasks of tag localization and identification based on the strength of the backscatter signal. We present a technique for estimation of amplitude and phase of the tag-to-tag channel that can be performed with very limited computational and energy resources. This enables monitoring of the interactions between tagged objects and activities around tags, as well as assessment of a variety of engineering structures. Experimental results demonstrate high resolution in the amplitude and phase channel measurement at a distances ranging from 22 cm to 1.34 m.

An inductive link based wireless power transfer methodology is described for a smart knee implant designed to continuously monitor the loads on the knee. The primary and secondary coil dimensions and wireless link characteristics are first optimized via an equivalent lumped electrical model. The wireless link is then simulated with more accurate 3D field simulations. The impact of the package and various layers of tissue between the coils on the quality of the link is investigated. The effects of potential misalignments between the coils are also analyzed. When the distance between the coils is 39 mm and the input power to the primary (external) coil is 2.5 W, approximately 0.35 mW of power can be received by the secondary coil that is located within the implant.

We present a wake-up receiver amenable to integration in a node of RF backscattering tag-to-tag network. A high input impedance of a passive envelope detector (ED) is accomplished by backward bias that improves the passive voltage gain. Two differential outputs are ac-coupled to a baseband amplifier that operates in the subthreshold region. We develop a closed-form model of the passive ED in order to predict the output and ripple voltages and therefor the receiver’s sensitivity. The wakeup receiver is implemented in 180 nm CMOS technology and consumes 2 nW with 0.8 V supply voltage while demodulating 915 MHz amplitude-shift keying (ASK) signal with data rate of 10 kbps. The receiver demonstrates -67.98 dBm sensitivity in resolving ASK modulated signal.

We present RF energy harvester and management strategy tailored for the passive near-zero power devices. Radio- less RF-powered backscattering tags that have the ability to recognize and localize activities in the surrounding environment are example of such devices. We propose a management strategy that determines the operation regime of the harvester based on the input power level at which harvester provides the instantaneous supply voltage for device operation. As the input power exceeds this level, the storage of the excess energy is managed by an adaptive capacitor charging circuit that keeps the voltage at the input of voltage regulator constant. We demonstrate that backscatter-based RF tag in the listening mode of operation can instantaneously operate with an input power of -34.4 dBm. Due to the adaptive capacitor charging circuit, the power efficiency of the energy harvester is higher than 50% over a range of input powers from -25 dBm up to -5 dBm.

Backscattering tags transmit passively without an on-board active radio transmitter. Almost all present-day backscatter systems, however, rely on active radio receivers. This presents a significant scalability, power and cost challenge for backscatter systems. To overcome this barrier, recent research has empowered these passive tags with the ability to reliably receive backscatter signals from other tags. This forms the building block of passive networks wherein tags talk to each other without an active radio on either the transmit or receive side. For wider functionality, accurate localization of such tags is critical. All known backscatter tag localization techniques rely on active receivers for measuring and characterizing the received signal. As a result, they cannot be directly applied to passive tag-to-tag networks. This paper overcomes the gap by developing a localization technique for such passive networks based on a novel method for phase-based ranging in passive receivers. This method allows pairs of passive tags to collaboratively determine the inter-tag channel phase while effectively minimizing the effects of multipath and noise in the surrounding environment. Building on this, we develop a localization technique that benefits from large link diversity uniquely available in a passive tag-to-tag network. We evaluate the performance of our techniques with extensive micro-benchmarking experiments in an indoor environment using fabricated prototypes of tag hardware. We show that our phase-based ranging performs similar to active receivers, providing median 1D ranging error <1 cm and median localization error also <1 cm. Benefiting from the large-scale link diversity our localization technique outperforms several state-of-the-art techniques that use active receivers.

In this paper, we present a technique whereby passive tags can track each other in a backscattering tag-to-tag network (BTTN). In such a network, passive tags without any on-board radio transceivers communicate directly with each other by backscattering an external excitation signal. First, we explain how the tags determine their distances to other communicating tags in their proximity and then how they can track nearby tags. Our technique is based on multiphase backscattering, more specifically, on the ability of backscattering tags to systematically change the phase offset of the signal that is being backscattered. A passive receiving tag with an envelope detector can then examine the received signal amplitude over the multiple backscattering phases and can draw inferences about the inter-tag distance. We demonstrate our method and show its accuracy on tags that we have built in our lab. Experiments show that our passive tags can measure Doppler shifts with approximately the same accuracy as that achieved by active conventional RFID readers. Our median tracking error based on data from two tags is only about 2.5 cm.

Free floating sub-mm and mm sized brain implants can communicate through a backscatter-based link in a presence of the EM field generated by the external coil. This link reduces the bandwidth requirement in the uplink communication of these implants to the external coil and enables a close-loop operation of the distributed implant system through reduced latency. The critical challenge in the link design stems from the low modulation index in the incident signal at the receiving coil. This calls for the design of the ASK demodulator that can resolve signals with low modulation index. We propose a demodulator design comprising a self-biased common-source based envelope detector that provides sufficient conversion gain and at the same time operates with a low power consumption. With 90 MHz carrier frequency and 50-kbps data rate, the ASK demodulator, implemented in 65 nm CMOS technology, resolves input RF signal with 1% modulation index consuming less than 100 nW when amplitude of the input RF signal is 200 mV.

We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this technique, we basically explore the backscatter channel by reflecting the incident RF signal with different changes in the phase. We define a measure of the backscatter channel, which we call backscatter channel state information (BCSI). The BCSI is composed of backscatter channel phase, backscatter amplitude, and change in baseline excitation level. When acquired over time, this measure provides rich RF analytics that can be used to extract various types of information from the environment of the tags by signal processing/machine learning methods. We show in the paper that this analytics is invariant w.r.t. to some variables including the deployment environment. We provide results from experiments with our tags that demonstrate the invariance of the BCSI.

The wirelessly powered adiabatic logic has introduced significant power savings in the design of the computational logic. We explore energy-efficient interfacing of one of the most efficient adiabatic logic families, pass-transistor adiabatic logic (PAL), with RF harvested signal. The interface circuit, signal shaper, transforms the bipolar sinusoidal input voltage to nonnegative unipolar sinusoidal output that serves as the power clock signal for PAL. A theoretical analysis of the operation of the signal shaper is presented and verified using simulations in 65 nm CMOS technology. The designed shaper, when interfaced with 8-bit multiplier implemented using PAL, demonstrates the settling time of a few clock periods and high power conversion efficiency as high as 90%.

We envision a future where every object in our living and working environment will carry one or more RF tags. Based on the backscattering tag-to-tag communication link, these RF tags will be connected in a network without the need for the central interrogating device. We present a novel tag architecture that enables estimation of the parameters of wireless tag-to-tag channel by a passive receiver. Sampling the received baseband signal at different reflecting phases at the backscattering tag enables estimation of amplitude and phase of the tag-to-tag channel. The low-power implementation of the channel estimator, after envelope detection, integrates amplification and filtering of the baseband signal that is followed by analog-to-digital conversion. The channel estimator, implemented in 65 nm CMOS technology, has sensitivity of -45 dBm at 2.5% modulation index and consumes 122 nW.

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.

A novel framework and related methodologies are described to leverage RF power for building intelligent and battery-free devices with communication and computation capabilities. These passive devices are envisioned to make significant impact for the popular vision of smart dust due to extreme low power operation. The communication framework relies on tag-to-tag backscattering with very limited energy resources. The computing framework relies on a novel AC computing methodology that facilitates local data processing with an order of magnitude less power consumption. These enabling technologies, as described in this paper, revitalize the concept of smart dust with significant impact on various application domains such as smart spaces, implantable devices, and environmental/structural monitoring.

We present the vision of BARNET (Backscattering Activity Recognition NEtwork of Tags), a network of passive RF tags that use RF backscatter for tag-to-tag communication. BARNET not only provides identification of tagged objects but also can serve as a 'device-free' activity recognition system. BARNET's key innovation is the concept of backscatter channel state information (BCSI) which can be measured via systematic multiphase probing of the backscatter tag-to-tag channel using innovative processing on the passive tags. So far such measurements were only possible using active radio receivers that consume much higher power. Changes in BCSI provide signatures for different activities in the environment that can be learned using suitable machine learning tools. We develop the BARNET tag architecture which shows that an ASIC implementation can run on harvested RF power. We develop a printed circuit board (PCB) prototype using discrete components to evaluate activity recognition performance. We show that the prototype can recognize human daily activities with an average error around 6%. Overall, BARNET uses passive tags to achieve the same level of performance as systems that use powered, active radios.

Radio frequency (RF)-powered backscatter communication between passive tags holds tremendous potential as an enabling technology for a ubiquitous “Internet of Things.” We develop a backscattering tag-to-tag network (BTTN), comprised of passive tags capable of large-scale, passive, and multihop 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 multiphase 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 radio frequency identification 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 3 m at 5 kb/s with an excitation power level of only -20 dBm while successfully overcoming phase cancellation. We further extend BTTN operation to a multihop network where we demonstrate a four hop link capable of communicating over 12 m under similar conditions.

Summary form only given. 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.

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.

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 the design of implantable devices, providing the wireless power to the device presents one of the critical parts of the system design and RF power harvesting through inductive coupling presents a commonly used solution. The maximum power transfer in the inductive link occurs when the transmitting and receiving circular coils are aligned on the same central axes. However, when there is either angular or lateral misalignment between the coils, the maximum delivered power to the receiving coil can be significantly reduced. We propose a design of an array of coils instead of a single transmitting coil that through adaptive control of the phase of the coil feed currents optimizes the power transfer when misalignment exists. We demonstrate that the two coil transmitting antenna provides increase in the power efficiency compared to the same area single coil when both angular and lateral misalignment exist in a smart pill application.

Limited energy is a significant challenge for IoT devices since frequent battery replacement is not feasible. Various energy harvesting techniques have been previously proposed to alleviate this challenge. A new circuit design technique is developed in this paper to significantly enhance the power efficiency of existing wireless energy harvesting methods. Contrary to the traditional approach, the rectification and regulation blocks are eliminated and the harvested signal is directly used to power the IoT device by leveraging charge-recycling circuit theory. In addition to higher energy-efficiency, the proposed approach also reduces the form factor and therefore lowers the cost of an IoT device. The methodology is evaluated using a 45 nm CMOS technology, demonstrating approximately five times reduction in power consumption compared to the traditional approach.

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.

We present a low-power wide-dynamic-range readout circuit that directly interfaces a selective metal-oxide gas sensor. The proposed novel readout architecture implements an adaptive baseline compensation and limits the sensor current. The readout IC can interface the sensors with the baseline resistance from 1 kΩ to 100 MΩ and measures the gas induced resistance change in the range from 0.05% to 10% of the baseline resistance. The simulations demonstrate the 166 dB dynamic range of the readout circuit and 113 μW power consumption amenable to the sensors that can operate at room temperature for the application of the proposed system in portable breath analyzers.

A low-noise readout integrated circuit, comprising a charge sensitive amplifier, a pulse shaper with baseline holder, a peak detector and an A/D converter, is presented. The designed IC quantifies optical response of a large-area epitaxial photodiode integrated on a body of a semiconductor scintillator. The input transistor size and the time constant of the shaper are optimized to obtain a minimum equivalent noise charge(ENC) with the large input load capacitance. A time-based clockless A/D converter is implemented to minimize the interference of the digital part of the readout system on the low-noise charge-sensitive amplifier. The simulated ENC of the readout system interfacing a 50 pF capacitance and a dark current of 10 pA that model the epitaxial photodiode is 172 electrons at 12 μs time constant of the pulse shaper with power consumption of CSA and shaper of 2.2 mW.

A design of low-noise charge sensitive amplifier (CSA) for measurement of optical response of photo-detector registering light produced by semiconductor scintillator is presented. Detailed analysis of the CSA suitable for large parasitic detector capacitance is provided, regarding noise, power and stability. Two scenarios where input transistor is biased in strong inversion and weak inversion are compared, with included accurate 1/f noise modeling. The experimental prototype was implemented in 0.5 μm CMOS process with a 5 V power supply.

A design of adaptive front-end preamplifier for measurement of optical response of epitaxial photodiode, registering light produced by semiconductor scintillator, is presented. A time constant of continuous-time based pulse shaping filter is adaptively determined to achieve optimal sensitivity for variable parameters of photodetector and readout electronics. Experimental prototype was designed in 0.5µm CMOS process.

Extended counting analog-to-digital converter (ECADC) combines the accuracy of delta-sigma modulation and the speed of algorithmic conversion. This conversion architecture is shown to be useful in biomedical applications, where both resolution and speed are demanded. This work presents a design of ECADC for 32 neural recording channels. Several power optimizing methods are described. The designed converter achieves a resolution of 13 bits and a sampling frequency of 512 kHz. With 3.3V supply, the total power consumption is estimated to be 7 mW. The whole system including 32 neural recording channels is fitted in an area of 3mm × 3mm in 0.5μm CMOS process.

For recording of neural signals from large population of neurons, stringent constraints are imposed on the design of neural amplifiers. We have designed neural amplifier in FD-SOI technology in order to achieve lower power consumption, smaller area, and better noise efficiency factor compared to the standard bulk processes. A symmetric pseudo resistor was realized with resistances on the order of 1015Ω, enabling a low cut-off frequency of 0.6mHz. The designed neural amplifier occupies an area of 0.004mm2, with simulated performance demonstrating an input-referred noise of 3.07μVrmsand a power consumption of 6μW.

We present micropower mixed-signal VLSI hardware for real-time blind separation and localization of acoustic sources. Gradient flow representation of the traveling wave signals acquired over a miniature (1cm diameter) array of four microphones yields linearly mixed instantaneous observations of the time-differentiated sources, separated and localized by independent component analysis (ICA). The gradient flow and ICA processors each measure 3mm 3mm in 0.5 m CMOS, and consume 54 W and 180 W power, respectively, from a 3 V supply at 16 ks/s sampling rate. Experiments demonstrate perceptually clear (12dB) separation and precise localization of two speech sources presented through speakers positioned at 1.5m from the array on a conference room table. Analysis of the multipath residuals shows that they are spectrally diffuse, and void of the direct path.

Gradient flow converts the problem of separating unknown delayed mixtures of sources, from traveling waves impinging on .an array of sensors, into a simpler problem of separating unknown instantaneous mixtures of the time-differentiated sources, obtained by acquiring or computing spatial and temporal derivatives on the array. The linear coefficients in the instantaneous mixture directly represent the delays, which in tum determine the direction angles of the sources. This formulation is attractive, since it allows to separate and localize waves of broadband signals using standard tools of independent component analysis (ICA), yielding the sources along with their direction angles. The technique is suited for arrays of small aperture, with dimensions shorter than the coherence length of the waves. We present gradient flow experiments on an array of four hearing aid microphones placed within a 5 mm radius, yielding 20 dB separation of joint speech in outdoors acoustic environments, and 10 dB separation indoors under mild reverberant conditions. These results suggest applications of gradient flow miniature microphone arrays to intelligent hearing aids with adaptive suppression of interfering signals and nonstationary noise.