Power efficient analog-to-digital converters using both voltage and time domain information

As advanced wired and wireless communication systems attempt to achieve higher performance, the demand for high resolution and wide signal bandwidth in their associated ADCs is strongly increased. Recently, time-domain quantization has drawn attention from its scalability in deep submicron CMOS processes. Furthermore, there are several interesting aspects of time-domain quantizer by processing the signal in time rather than only in voltage domain especially for power efficiency. This research focuses on developing a new architecture for power efficient, high resolution ADCs using both voltage and time domain information. As a first approach, a new ΔƩ ADC based on a noise-shaped two-step integrating quantizer which quantizes the signal in voltage and time domains is presented. Attaining an extra order of noise-shaping from the integrating quantizer, the proposed ΔƩ ADC manifests a second-order noise-shaping with a first-order loop filter. Furthermore, this quantizer provides an 8b uantization in itself, drastically reducing the oversampling requirement. The proposed ADC also incorporates a new feedback DAC topology that alleviates the feedback DAC complexity of a two-step 8b quantizer. The measured results of the prototype ADC implemented in a 0.13μm CMOS demonstrate peak SNDR of 70.7dB (11.5b ENOB) at 8.1mW power, with an 8x OSR at 80MHz sampling frequency. To further improve ADC performance, a Nyquist ADC based on a time-based pipelined TDC is also proposed as a second approach. In this work, a simple V-T conversion scheme with a cheap low gain amplifier in its first stage and a hybrid time-domain quantization stage based on simple charge pump and capacitive DAC in its backend stages, are also proposed to improve ADC linearity and power efficiency. Using voltage and time domain information, the proposed ADC architecture is beneficial for both resolution and power efficiency, with MSBs resolved in voltage domain and LSBs in time domain. The measured results of the prototype ADC implemented in a 0.13μm CMOS demonstrate peak SNDR of 69.3dB (11.2b ENOB) at 6.38mW power and 70MHz sampling frequency. The FOM is 38.2fJ/conversion-step

DESIGN OF LOW-POWER LOW-VOLTAGE SUCCESSIVE-APPROXIMATION ANALOG-TO-DIGITAL CONVERTERS

Ph.DDOCTOR OF PHILOSOPH

Design and Analysis of a Low-Power 8-Bit 500 KS/S SAR ADC for Bio-Medical Implant Devices

This thesis project involves the design and analysis of an 8-bit Successive Approximation Register (SAR) Analog to Digital Convertor (ADC), designed for low- power applications such as bio-medical implants. The sampling rate for this ADC is 500 KS/s. The power consumption for the whole SAR ADC system was measured to be 2.1 uW. The novelty of this project is the proposal of an extremely energy efficient comparator architecture. The result is the design of a final ADC with reasonable sampling speed, accuracy and low power consumption. In this project, all the different subsystems have been designed at the transistor level with 45 nm CMOS technology. The logical circuit was designed using Verilog language. It was then synthesized and integrated in the overall system

Concepts for smart AD and DA converters

This thesis studies the `smart' concept for application to analog-to-digital and digital-to-analog converters. The smart concept aims at improving performance - in a wide sense - of AD/DA converters by adding on-chip intelligence to extract imperfections and to correct for them. As the smart concept can correct for certain imperfections, it can also enable the use of more efficient architectures, thus yielding an additional performance boost. Chapter 2 studies trends and expectations in converter design with respect to applications, circuit design and technology evolution. Problems and opportunities are identfied, and an overview of performance criteria is given. Chapter 3 introduces the smart concept that takes advantage of the expected opportunities (described in chapter 2) in order to solve the anticipated problems. Chapter 4 applies the smart concept to digital-to-analog converters. In the discussed example, the concept is applied to reduce the area of the analog core of a current-steering DAC. It is shown that a sub-binary variable-radix approach reduces the area of the current-source elements substantially (10x compared to state-of-the-art), while maintaining accuracy by a self-measurement and digital pre-correction scheme. Chapter 5 describes the chip implementation of the sub-binary variable-radix DAC and discusses the experimental results. The results confirm that the sub-binary variable-radix design can achieve the smallest published current-source-array area for the given accuracy (12bit). Chapter 6 applies the smart concept to analog-to-digital converters, with as main goal the improvement of the overall performance in terms of a widely used figure-of-merit. Open-loop circuitry and time interleaving are shown to be key to achieve high-speed low-power solutions. It is suggested to apply a smart approach to reduce the effect of the imperfections, unintentionally caused by these key factors. On high-level, a global picture of the smart solution is proposed that can solve the problems while still maintaining power-efficiency. Chapter 7 deals with the design of a 500MSps open-loop track-and-hold circuit. This circuit is used as a test case to demonstrate the proposed smart approaches. Experimental results are presented and compared against prior art. Though there are several limitations in the design and the measurement setup, the measured performance is comparable to existing state-of-the-art. Chapter 8 introduces the first calibration method that counteracts the accuracy issues of the open-loop track-and-hold. A description of the method is given, and the implementation of the detection algorithm and correction circuitry is discussed. The chapter concludes with experimental measurement results. Chapter 9 introduces the second calibration method that targets the accuracy issues of time-interleaved circuits, in this case a 2-channel version of the implemented track-and-hold. The detection method, processing algorithm and correction circuitry are analyzed and their implementation is explained. Experimental results verify the usefulness of the method

PROCESS AWARE ANALOG-CENTRIC SINGLE LEAD ECG ACQUISITION AND CLASSIFICATION CMOS FRONTEND

The primary objective of this research work is the development of a low power single-lead ECG analog front-end (AFE) architecture which includes acquisition, digitization, process aware efficient gain and frequency control mechanism and a low complexity classifier for the detecting asystole, extreme bardycardia and tachycardia. Recent research on ECG recording systems focuses on the design of a compact single-lead wearable/portable devices with ultra-low-power consumption and in-built hardware for diagnosis and prognosis. Since, the amplitude of the ECG signal varies from hundreds of µV to a few mV, and has a bandwidth of DC to 250 Hz, conventional front-ends use an instrument amplifier followed by a programmable gain amplifier (PGA) to amplify the input ECG signal appropriately. This work presents an mixed signal ECG fronted with an ultra-low power two-stage capacitive-coupled signal conditioning circuit (or an AFE), providing programmable amplification along with tunable 2nd order high pass and lowpass filter characteristics. In the contemporary state-of-the-art ECG recording systems, the gain of the amplifier is controlled by external digital control pins which are in turn dynamically controlled through a DSP. Therefore, an efficient automatic gain control mechanism with minimal area overhead and consuming power in the order of nano watts only. The AGC turns the subsequent ADC on only after output of the PGA (or input of the ADC) reaches a level for which the ADC achieves maximum signal-to-noise-ratio (SNR), hence saving considerable startup power and avoiding the use of DSP. Further, in any practical filter design, the low pass cut-off frequency is prone to deviate from its nominal value across process and temperature variations. Therefore, post-fabrication calibration is essential, before the signal is fed to an ADC, to minimize this deviation, prevent signal degradation due to aliasing of higher frequencies into the bandwidth for classification of ECG signals, to switch to low resolution processing, hence saving power and enhances battery lifetime. Another short-coming noticed in the literature published so far is that the classification algorithm is implemented in digital domain, which turns out to be a power hungry approach. Moreover, Although analog domain implementations of QRS complexes detection schemes have been reported, they employ an external micro-controller to determine the threshold voltage. In this regard, finally a power-efficient low complexity CMOS fully analog classifier architecture and a heart rate estimator is added to the above scheme. It reduces the overall system power consumption by reducing the computational burden on the DSP. The complete proposed scheme consists of (i) an ultra-low power QRS complex detection circuit using an autonomous dynamic threshold voltage, hence discarding the need of any external microcontroller/DSP and calibration (ii) a power efficient analog classifier for the detection of three critical alarm types viz. asystole, extreme bradycardia and tachycardia. Additionally, a heart rate estimator that provides the number of QRS complexes within a period of one minute for cardiac rhythm (CR) and heart rate variability (HRV) analysis. The complete proposed architecture is implemented in UMC 0.18 µm CMOS technology with 1.8 V supply. The functionality of each of the individual blocks are successfully validated using postextraction process corner simulations and through real ECG test signals taken from the PhysioNet database. The capacitive feedback amplifier, Σ∆ ADC, AGC and the AFT are fabricated, and the measurement results are discussed here. The analog classification scheme is successfully validated using embed NXP LPC1768 board, discrete peak detector prototype and FPGA software interfac