Architectures and Circuit Techniques for High-Performance Field-Programmable CMOS Software Defined Radios

Next-generation wireless communication systems put more stringent performance requirements on the wireless RF receiver circuits. Sensitivity, linearity, bandwidth and power consumption are some of the most important specifications that often face tightly coupled tradeoffs between them. To increase the data throughput, a large number of fragmented spectrums are being introduced to the wireless communication standards. Carrier aggregation technology needs concurrent communication across several non-contiguous frequency bands, which results in a rapidly growing number of band combinations. Supporting all the frequency bands and their aggregation combinations increases the complexity of the RF receivers. Highly flexible software defined radio (SDR) is a promising technology to address these applications scenarios with lower complexity by relaxing the specifications of the RF filters or eliminating them. However, there are still many technology challenges with both the receiver architecture and the circuit implementations. The performance requirements of the receivers can also vary across different application scenario and RF environments. Field-programmable dynamic performance tradeoff can potentially reduce the power consumption of the receiver. In this dissertation, we address the performance enhancement challenges in the wideband SDRs by innovations at both the circuit building block level and the receiver architecture level. A series of research projects are conducted to push the state-of-the-art performance envelope and add features such as field-programmable performance tradeoff and concurrent reception. The projects originate from the concept of thermal noise canceling techniques and further enhance the RF performance and add features for more capable SDR receivers. Four generations of prototype LNA or receiver chips are designed, and each of them pushes at least one aspect of the RF performance such as bandwidth, linearity, and NF. A noise-canceling distributed LNA breaks the tradeoff between NF and RF bandwidth by introducing microwave circuit techniques from the distributed amplifiers. The LNA architecture uniquely provides ultra high bandwidth and low NF at low frequencies. A family of field-programmable LNA realized field-programmable performance tradeoff with current-reuse programmable transconductance cells. Interferer-reflecting loops can be applied around the LNAs to improve their input linearity by rejecting the out-of-band interferers with a wideband low in- put impedance. A low noise transconductance amplifier (LNTA) that operates in class-AB-C is invented to can handle rail-to-rail out-of-band blocker without saturation. Class-AB and class-C transconductors form a composite amplifier to increase the linear range of the input voltage. A new antenna interface named frequency-translational quadrature-hybrid (FTQH) breaks the input impedance matching requirement of the LNAs by introducing quadrature hybrid couplers to the CMOS RFIC design. The FTQH receiver achieves wideband sub-1dB NF and supports scalable massive frequency-agile concurrent reception

Digitally Interfaced Analog Correlation Filter System for Object Tracking Applications

Advanced correlation filters have been employed in a wide variety of image processing and pattern recognition applications such as automatic target recognition and biometric recognition. Among those, object recognition and tracking have received more attention recently due to their wide range of applications such as autonomous cars, automated surveillance, human-computer interaction, and vehicle navigation.Although digital signal processing has long been used to realize such computational systems, they consume extensive silicon area and power. In fact, computational tasks that require low to moderate signal-to-noise ratios are more efficiently realized in analog than digital. However, analog signal processing has its own caveats. Mainly, noise and offset accumulation which degrades the accuracy, and lack of a scalable and standard input/output interface capable of managing a large number of analog data.Two digitally-interfaced analog correlation filter systems are proposed. While digital interfacing provided a standard and scalable way of communication with pre- and post-processing blocks without undermining the energy efficiency of the system, the multiply-accumulate operations were performed in analog. Moreover, non-volatile floating-gate memories are utilized as storage for coefficients. The proposed systems incorporate techniques to reduce the effects of analog circuit imperfections.The first system implements a 24x57 Gilbert-multiplier-based correlation filter. The I/O interface is implemented with low-power D/A and A/D converters and a correlated double sampling technique is implemented to reduce offset and lowfrequency noise at the output of analog array. The prototype chip occupies an area of 3.23mm2 and demonstrates a 25.2pJ/MAC energy-efficiency at 11.3 kVec/s and 3.2% RMSE.The second system realizes a 24x41 PWM-based correlation filter. Benefiting from a time-domain approach to multiplication, this system eliminates the need for explicit D/A and A/D converters. Careful utilization of clock and available hardware resources in the digital I/O interface, along with application of power management techniques has significantly reduced the circuit complexity and energy consumption of the system. Additionally, programmable transconductance amplifiers are incorporated at the output of the analog array for offset and gain error calibration. The prototype system occupies an area of 0.98mm2 and is expected to achieve an outstanding energy-efficiency of 3.6pJ/MAC at 319kVec/s with 0.28% RMSE

Low-Power Wireless Medical Systems and Circuits for Invasive and Non-Invasive Applications

Approximately 75% of the health care yearly budget of public health systems around the world is spent on the treatment of patients with chronic diseases. This, along with advances on the medical and technological fields has given rise to the use of preventive medicine, resulting on a high demand of wireless medical systems (WMS) for patient monitoring and drug safety research. In this dissertation, the main design challenges and solutions for designing a WMS are addressed from system-level, using off-the-shell components, to circuit implementation. Two low-power oriented WMS aiming to monitor blood pressure of small laboratory animals (implantable) and cardiac-activity (12-lead electrocardiogram) of patients with chronic diseases (wearable) are presented. A power consumption vs. lifetime analysis to estimate the monitoring unit lifetime for each application is included. For the invasive/non-invasive WMS, in-vitro test benches are used to verify their functionality showing successful communication up to 2.1 m/35 m with the monitoring unit consuming 0.572 mA/33 mA from a 3 V/4.5 V power supply, allowing a two-year/ 88-hour lifetime in periodic/continuous operation. This results in an improvement of more than 50% compared with the lifetime commercial products. Additionally, this dissertation proposes transistor-level implementations of an ultra-low-noise/low-power biopotential amplifier and the baseband section of a wireless receiver, consisting of a channel selection filter (CSF) and a variable gain amplifier (VGA). The proposed biopotential amplifier is intended for electrocardiogram (ECG)/ electroencephalogram (EEG)/ electromyogram (EMG) monitoring applications and its architecture was designed focused on improving its noise/power efficiency. It was implemented using the ON-SEMI 0.5 µm standard process with an effective area of 360 µm2. Experimental results show a pass-band gain of 40.2 dB (240 mHz - 170 Hz), input referred noise of 0.47 Vrms, minimum CMRR of 84.3 dBm, NEF of 1.88 and a power dissipation of 3.5 µW. The CSF was implemented using an active-RC 4th order inverse-chebyshev topology. The VGA provides 30 gain steps and includes a DC-cancellation loop to avoid saturation on the sub-sequent analog-to-digital converter block. Measurement results show a power consumption of 18.75 mW, IIP3 of 27.1 dBm, channel rejection better than 50 dB, gain variation of 0-60dB, cut-off frequency tuning of 1.1-2.29 MHz and noise figure of 33.25 dB. The circuit was implemented in the standard IBM 0.18 µm CMOS process with a total area of 1.45 x 1.4 mm^(2). The presented WMS can integrate the proposed biopotential amplifier and baseband section with small modifications depending on the target signal while using the low-power-oriented algorithm to obtain further power optimization

Expanding the role of functional mri in rehabilitation research

Functional magnetic resonance imaging (fMRI) based on blood oxygenation level dependent (BOLD) contrast has become a universal methodology in functional neuroimaging. However, the BOLD signal consists of a mix of physiological parameters and has relatively poor reproducibility. As fMRI becomes a prominent research tool for rehabilitation studies involving repeated measures of the human brain, more quantitative and stable fMRI contrasts are needed. This dissertation enhances quantitative measures to complement BOLD fMRI. These additional markers, cerebral blood flow (CBF) and cerebral blood volume (CBV) (and hence cerebral metabolic rate of oxygen (CMRO₂) modeling) are more specific imaging markers of neuronal activity than BOLD. The first aim of this dissertation assesses feasibility of complementing BOLD with quantitative fMRI measures in subjects with central visual impairment. Second, image acquisition and analysis are developed to enhance quantitative fMRI by quantifying CBV while simultaneously acquiring CBF and BOLD images. This aim seeks to relax assumptions related to existing methods that are not suitable for patient populations. Finally, CBF acquisition using a low-cost local labeling coil, which improves image quality, is combined with simultaneous acquisition of two types of traditional BOLD contrast. The demonstrated enhancement of CBF, CBV and CMRO₂measures can lead to better characterization of pathophysiology and treatment effects.Ph.D.Committee Chair: Hu, Xiaoping; Committee Member: Benkeser, Paul; Committee Member: Keilholz, Shella; Committee Member: Sathian, Krish; Committee Member: Schuchard, Ronal

Synthesis and characterization of biodegradable lignin nanoparticles with tunable surface properties

Lignin nanoparticles can serve as biodegradable carriers of biocidal actives with minimal environmental footprint. Here we describe the colloidal synthesis and interfacial design of nanoparticles with tunable surface properties using two different lignin precursors, Kraft (Indulin AT) lignin and Organosolv (high-purity lignin). The green synthesis process is based on flash precipitation of dissolved lignin polymer, which enabled the formation of nanoparticles in the size range of 45–250 nm. The size evolution of the two types of lignin particles is fitted on the basis of modified diffusive growth kinetics and mass balance dependencies. The surface properties of the nanoparticles are fine-tuned by coating them with a cationic polyelectrolyte, poly(diallyldimethylammonium chloride). We analyze how the colloidal stability and dispersion properties of these two types of nanoparticles vary as a function of pH and salinities. The data show that the properties of the nanoparticles are governed by the type of lignin used and the presence of polyelectrolyte surface coating. The coating allows the control of the nanoparticles’ surface charge and the extension of their stability into strongly basic regimes, facilitating their potential application at extreme pH conditions

Low-Noise Micro-Power Amplifiers for Biosignal Acquisition

There are many different types of biopotential signals, such as action potentials (APs), local field potentials (LFPs), electromyography (EMG), electrocardiogram (ECG), electroencephalogram (EEG), etc. Nerve action potentials play an important role for the analysis of human cognition, such as perception, memory, language, emotions, and motor control. EMGs provide vital information about the patients which allow clinicians to diagnose and treat many neuromuscular diseases, which could result in muscle paralysis, motor problems, etc. EEGs is critical in diagnosing epilepsy, sleep disorders, as well as brain tumors. Biopotential signals are very weak, which requires the biopotential amplifier to exhibit low input-referred noise. For example, EEGs have amplitudes from 1 μV [microvolt] to 100 μV [microvolt] with much of the energy in the sub-Hz [hertz] to 100 Hz [hertz] band. APs have amplitudes up to 500 μV [microvolt] with much of the energy in the 100 Hz [hertz] to 7 kHz [hertz] band. In wearable/implantable systems, the low-power operation of the biopotential amplifier is critical to avoid thermal damage to surrounding tissues, preserve long battery life, and enable wirelessly-delivered or harvested energy supply. For an ideal thermal-noise-limited amplifier, the amplifier power is inversely proportional to the input-referred noise of the amplifier. Therefore, there is a noise-power trade-off which must be well-balanced by the designers. In this work I propose novel amplifier topologies, which are able to significantly improve the noise-power efficiency by increasing the effective transconductance at a given current. In order to reject the DC offsets generated at the tissue-electrode interface, energy-efficient techniques are employed to create a low-frequency high-pass cutoff. The noise contribution of the high-pass cutoff circuitry is minimized by using power-efficient configurations, and optimizing the biasing and dimension of the devices. Sufficient common-mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are achieved to suppress common-mode interferences and power supply noises. Our design are fabricated in standard CMOS processes. The amplifiers’ performance are measured on the bench, and also demonstrated with biopotential recordings

Fault and Defect Tolerant Computer Architectures: Reliable Computing With Unreliable Devices

This research addresses design of a reliable computer from unreliable device technologies. A system architecture is developed for a fault and defect tolerant (FDT) computer. Trade-offs between different techniques are studied and yield and hardware cost models are developed. Fault and defect tolerant designs are created for the processor and the cache memory. Simulation results for the content-addressable memory (CAM)-based cache show 90% yield with device failure probabilities of 3 x 10(-6), three orders of magnitude better than non fault tolerant caches of the same size. The entire processor achieves 70% yield with device failure probabilities exceeding 10(-6). The required hardware redundancy is approximately 15 times that of a non-fault tolerant design. While larger than current FT designs, this architecture allows the use of devices much more likely to fail than silicon CMOS. As part of model development, an improved model is derived for NAND Multiplexing. The model is the first accurate model for small and medium amounts of redundancy. Previous models are extended to account for dependence between the inputs and produce more accurate results