Design of Energy-Efficient A/D Converters with Partial Embedded Equalization for High-Speed Wireline Receiver Applications

As the data rates of wireline communication links increases, channel impairments such as skin effect, dielectric loss, fiber dispersion, reflections and cross-talk become more pronounced. This warrants more interest in analog-to-digital converter (ADC)-based serial link receivers, as they allow for more complex and flexible back-end digital signal processing (DSP) relative to binary or mixed-signal receivers. Utilizing this back-end DSP allows for complex digital equalization and more bandwidth-efficient modulation schemes, while also displaying reduced process/voltage/temperature (PVT) sensitivity. Furthermore, these architectures offer straightforward design translation and can directly leverage the area and power scaling offered by new CMOS technology nodes. However, the power consumption of the ADC front-end and subsequent digital signal processing is a major issue. Embedding partial equalization inside the front-end ADC can potentially result in lowering the complexity of back-end DSP and/or decreasing the ADC resolution requirement, which results in a more energy-effcient receiver. This dissertation presents efficient implementations for multi-GS/s time-interleaved ADCs with partial embedded equalization. First prototype details a 6b 1.6GS/s ADC with a novel embedded redundant-cycle 1-tap DFE structure in 90nm CMOS. The other two prototypes explain more complex 6b 10GS/s ADCs with efficiently embedded feed-forward equalization (FFE) and decision feedback equalization (DFE) in 65nm CMOS. Leveraging a time-interleaved successive approximation ADC architecture, new structures for embedded DFE and FFE are proposed with low power/area overhead. Measurement results over FR4 channels verify the effectiveness of proposed embedded equalization schemes. The comparison of fabricated prototypes against state-of-the-art general-purpose ADCs at similar speed/resolution range shows comparable performances, while the proposed architectures include embedded equalization as well

Fully-passive switched-capacitor techniques for high performance SAR ADC design

In recent years, SAR ADC becomes more and more popular in various low-power applications such as wireless sensors and low energy radios due to its circuit simplicity, high power efficiency, and scaling compatibility. However, its speed is limited by its successive approximation procedures and its power efficiency greatly reduces with the ADC resolution going beyond 10 bit. To address these issues, this thesis proposes to embed two techniques: 1) compressive sensing (CS) and 2) noise shaping (NS) to a conventional SAR ADC. The realization of both techniques are based on fully-passive switched-capacitor techniques. CS is a recently emerging sampling paradigm, stating that the sparsity of a signal can be exploited to reduce the ADC sampling rate below the Nyquist rate. Different from conventional CS frameworks which require dedicated analog CS encoders, this thesis proposes a fully-passive CS-SAR ADC architecture which only requires minor modification to a conventional SAR ADC. Two chips are fabricated in a 0.13 µm process to prove the concept. One chip is a single-channel CS-SAR ADC which can reduce the ADC conversion rate by 4 times, thus reducing the ADC power by 4 times. In many wireless sensing applications, multiple ADCs are commonly required to sense multi-channel signals such as multi-lead ECG sensing and parallel neural recording. Therefore, the other chip is a multi-channel CS-SAR ADC which can simultaneously convert 4-channel signals with a sampling rate of one channel’s Nyquist rate. At 0.8 V and 1 MS/s, both chips achieve an effective Walden FoM of around 5 fJ/conversion-step. This thesis also proposes a novel NS SAR ADC architecture that is simple, robust and low power for high-resolution applications. Compared to conventional ∆Σ ADCs, it replaces the power-hungry active integrator with a passive integrator which only requires one switch and two capacitors. Compared to previous 1st-order NS SAR ADC works, it achieves the best NS performance and can be easily extended to 2nd-order. A 1st-order 10-bit NS SAR ADC is fabricated in a 0.13 µm process. Through NS, SNDR increases by 6 dB with OSR doubled, achieving a 12- bit ENOB at OSR = 8. An improved version of a 2nd-order 9-bit NS SAR ADC is designed and simulated in a 40 nm process. The SNDR increases by 10 dB with OSR doubled, achieving a 14-bit ENOB at OSR = 16. At a bandwidth of 312.5 kHz, the Schreier FoM is 181 dB and the Walden FoM is 12.5 fJ/conversion-step, proving that the proposed NS SAR ADC architecture can achieve high resolution and high power efficiency simultaneously.Electrical and Computer Engineerin

A 0.0022 mm² 10 bit 20 MS/s SAR ADC with Passive Single-Ended-to-Differential-Converter

This paper proposes a passive switched-capacitor single-ended-to-differential-converter (SDC) as a front-end of a differential SAR ADC, such that it can convert single-ended input signals. As the SDC is passive, the overall solution is power-efficient compared to active SDC solutions, and is especially suitable for lower/medium resolutions. As opposed to active SDC solutions with a static bias current, the proposed switched-capacitor network only consumes dynamic power, such that its consumption scales linearly with the sampling frequency. This paper discusses the basic concept of the proposed scheme, and analyzes the impact of noise and other imperfections, describes the trade-offs for power and area, and discusses the consequences for the input driver. A prototype implementation in 65nm CMOS achieves a figure-of-merit of 6.1fJ/conversion-step at 20MS/s, while reaching an SNDR of 54.7dB up to Nyquist and occupying a chip area of only 60μ m times 36μ m.</p

Ultra-low power mixed-signal frontend for wearable EEGs

Electronics circuits are ubiquitous in daily life, aided by advancements in the chip design industry, leading to miniaturised solutions for typical day to day problems. One of the critical healthcare areas helped by this advancement in technology is electroencephalography (EEG). EEG is a non-invasive method of tracking a person's brain waves, and a crucial tool in several healthcare contexts, including epilepsy and sleep disorders. Current ambulatory EEG systems still suffer from limitations that affect their usability. Furthermore, many patients admitted to emergency departments (ED) for a neurological disorder like altered mental status or seizures, would remain undiagnosed hours to days after admission, which leads to an elevated rate of death compared to other conditions. Conducting a thorough EEG monitoring in early-stage could prevent further damage to the brain and avoid high mortality. But lack of portability and ease of access results in a long wait time for the prescribed patients. All real signals are analogue in nature, including brainwaves sensed by EEG systems. For converting the EEG signal into digital for further processing, a truly wearable EEG has to have an analogue mixed-signal front-end (AFE). This research aims to define the specifications for building a custom AFE for the EEG recording and use that to review the suitability of the architectures available in the literature. Another critical task is to provide new architectures that can meet the developed specifications for EEG monitoring and can be used in epilepsy diagnosis, sleep monitoring, drowsiness detection and depression study. The thesis starts with a preview on EEG technology and available methods of brainwaves recording. It further expands to design requirements for the AFE, with a discussion about critical issues that need resolving. Three new continuous-time capacitive feedback chopped amplifier designs are proposed. A novel calibration loop for setting the accurate value for a pseudo-resistor, which is a crucial block in the proposed topology, is also discussed. This pseudoresistor calibration loop achieved the resistor variation of under 8.25%. The thesis also presents a new design of a curvature corrected bandgap, as well as a novel DDA based fourth-order Sallen-Key filter. A modified sensor frontend architecture is then proposed, along with a detailed analysis of its implementation. Measurement results of the AFE are finally presented. The AFE consumed a total power of 3.2A (including ADC, amplifier, filter, and current generation circuitry) with the overall integrated input-referred noise of 0.87V-rms in the frequency band of 0.5-50Hz. Measurement results confirmed that only the proposed AFE achieved all defined specifications for the wearable EEG system with the smallest power consumption than state-of-art architectures that meet few but not all specifications. The AFE also achieved a CMRR of 131.62dB, which is higher than any studied architectures.Open Acces

High speed – energy efficient successive approximation analog to digital converter using tri-level switching

This thesis reports issues and design methods used to achieve high-speed and high-resolution Successive Approximation Register analog to digital converters (SAR ADCs). A major drawback of this technique relates to the mismatch in the binary ratios of capacitors which causes nonlinearity. Another issue is the use of large capacitors due to nonlinear effect of parasitic capacitance. Nonlinear effect of capacitor mismatch is investigated in this thesis. Based on the analysis, a new Tri-level switching algorithm is proposed to reduce the matching requirement for capacitors in SAR ADCs. The integral non-linearity (INL) and the differential non-linearity (DNL) of the proposed scheme are reduced by factor of two over conventional SAR ADC, which is the lowest compared to the previously reported schemes. In addition, the switching energy of the proposed scheme is reduced by 98.02% compared with the conventional SAR architecture. A new correction method to solve metastability error of comparator based on a novel design approach is proposed which reduces the required settling time about 1.1τ for each conversion cycle. Based on the above proposed methods two SAR ADCs: an 8-bit SAR ADC with 50MS/sec sampling rate, and a 10-bit SAR split ADC with 70 MS/sec sampling rate have been designed in 0.18μm Silterra complementary metal oxide semiconductor (CMOS) technology process which works at 1.2V supply voltage and input voltage of 2.4Vp-p. The 8-bit ADC digitizes 25MHz input signal with 48.16dB signal to noise and distortion ratio (SNDR) and 52.41dB spurious free dynamic range (SFDR) while consuming about 589μW. The figure of merit (FOM) of this ADC is 56.65 fJ/conv-step. The post layout of the 10-bit ADC with 1MHz input frequency produces SNDR, SFDR and effective number of bits (ENOB) of 57.1dB, 64.05dB and 9.17Bit, respectively, while its DNL and INL are -0.9/+2.8 least significant bit (LSB) and -2.5/+2.7 LSB, respectively. The total power consumption, including digital, analog and reference power, is 1.6mW. The FOM is 71.75fJ/conv. step

Analog and Mixed Signal Design towards a Miniaturized Sleep Apnea Monitoring Device

Sleep apnea is a sleep-induced breathing disorder with symptoms of momentary and often repetitive cessations in breathing rhythm or sustained reductions in breathing amplitude. The phenomenon is known to occur with varying degrees of severity in literally millions of people around the world and cause a range of chronicle health issues. In spite of its high prevalence and serious consequences, nearly 80% of people with sleep apnea condition remain undiagnosed. The current standard diagnosis technique, termed polysomnography or PSG, requires the patient to schedule and undergo a complex full-night sleep study in a specially-equipped sleep lab. Due to both high cost and substantial inconvenience, millions of apnea patients are still undiagnosed and thus untreated. This research work aims at a simple, reliable, and miniaturized solution for in-home sleep apnea diagnosis purposes. The proposed solution bears high-level integration and minimal interference with sleeping patients, allowing them to monitor their apnea conditions at the comfort of their homes. Based on a MEMS sensor and an effective apnea detection algorithm, a low-cost single-channel apnea screening solution is proposed. A custom designed IC chip implements the apnea detection algorithm using time-domain signal processing techniques. The chip performs autonomous apnea detection and scoring based on the patient’s airflow signals detected by the MEMS sensor. Variable sensitivity is enabled to accommodate different breathing signal amplitudes. The IC chip was fabricated in standard 0.5-μm CMOS technology. A prototype device was designed and assembled including a MEMS sensor, the apnea detection IC chip, a PSoC platform, and wireless transceiver for data transmission. The prototype device demonstrates a valuable screening solution with great potential to reach the broader public with undiagnosed apnea conditions. In a battery-operated miniaturized medical device, an energy-efficient analog-to-digital converter is an integral part linking the analog world of biomedical signals and the digital domain with powerful signal processing capabilities. This dissertation includes the detailed design of a successive approximation register (SAR) ADC for ultra-low power applications. The ADC adopts an asynchronous 2b/step scheme that halves both conversion time and DAC/digital circuit’s switching activities to reduce static and dynamic energy consumption. A low-power sleep mode is engaged at the end of all conversion steps during each clock period. The technical contributions of this ADC design include an innovative 2b/step reference scheme based on a hybrid R-2R/C-3C DAC, an interpolation-assisted time-domain 2b comparison scheme, and a TDC with dual-edge-comparison mechanism. The prototype ADC was fabricated in 0.18μm CMOS process with an active area of 0.103 mm^(2), and achieves an ENoB of 9.2 bits and an FoM of 6.7 fJ/conversion-step at 100-kS/s

A Low-Power, Reconfigurable, Pipelined ADC with Automatic Adaptation for Implantable Bioimpedance Applications

Biomedical monitoring systems that observe various physiological parameters or electrochemical reactions typically cannot expect signals with fixed amplitude or frequency as signal properties can vary greatly even among similar biosignals. Furthermore, advancements in biomedical research have resulted in more elaborate biosignal monitoring schemes which allow the continuous acquisition of important patient information. Conventional ADCs with a fixed resolution and sampling rate are not able to adapt to signals with a wide range of variation. As a result, reconfigurable analog-to-digital converters (ADC) have become increasingly more attractive for implantable biosensor systems. These converters are able to change their operable resolution, sampling rate, or both in order convert changing signals with increased power efficiency. Traditionally, biomedical sensing applications were limited to low frequencies. Therefore, much of the research on ADCs for biomedical applications focused on minimizing power consumption with smaller bias currents resulting in low sampling rates. However, recently bioimpedance monitoring has become more popular because of its healthcare possibilities. Bioimpedance monitoring involves injecting an AC current into a biosample and measuring the corresponding voltage drop. The frequency of the injected current greatly affects the amplitude and phase of the voltage drop as biological tissue is comprised of resistive and capacitive elements. For this reason, a full spectrum of measurements from 100 Hz to 10-100 MHz is required to gain a full understanding of the impedance. For this type of implantable biomedical application, the typical low power, low sampling rate analog-to-digital converter is insufficient. A different optimization of power and performance must be achieved. Since SAR ADC power consumption scales heavily with sampling rate, the converters that sample fast enough to be attractive for bioimpedance monitoring do not have a figure-of-merit that is comparable to the slower converters. Therefore, an auto-adapting, reconfigurable pipelined analog-to-digital converter is proposed. The converter can operate with either 8 or 10 bits of resolution and with a sampling rate of 0.1 or 20 MS/s. Additionally, the resolution and sampling rate are automatically determined by the converter itself based on the input signal. This way, power efficiency is increased for input signals of varying frequency and amplitude

700mV low power low noise implantable neural recording system design

This dissertation presents the work for design and implementation of a low power, low noise neural recording system consisting of Bandpass Amplifier and Pipelined Analog to Digital Converter (ADC) for recording neural signal activities. A low power, low noise two stage neural amplifier for use in an intelligent Radio-Frequency Identification (RFID) based on folded cascode Operational Transconductance Amplifier (OTA) is utilized to amplify the neural signals. The optimization of the number of amplifier stages is discussed to achieve the minimum power and area consumption. The amplifier power supply is 0.7V. The midband gain of amplifier is 58.4dB with a 3dB bandwidth from 0.71 to 8.26 kHz. Measured input-referred noise and total power consumption are 20.7 μVrms and 1.90 μW respectively. The measured result shows that the optimizing the number of stages can achieve lower power consumption and demonstrates the neural amplifier's suitability for instu neutral activity recording. The advantage of power consumption of Pipelined ADC over Successive Approximation Register (SAR) ADC and Delta-Sigma ADC is discussed. An 8 bit fully differential (FD) Pipeline ADC for use in a smart RFID is presented in this dissertation. The Multiplying Digital to Analog Converter (MDAC) utilizes a novel offset cancellation technique robust to device leakage to reduce the input drift voltage. Simulation results of static and dynamic performance show this low power Pipeline ADC is suitable for multi-channel neural recording applications. The performance of all proposed building blocks is verified through test chips fabricated in IBM 180nm CMOS process. Both bench-top and real animal test results demonstrate the system's capability of recording neural signals for neural spike detection