High linearity analog and mixed-signal integrated circuit design

Linearity is one of the most important specifications in electrical circuits.;In Chapter 1, a ladder-based transconductance networks has been adopted first time to build a low distortion analog filters for low frequency applications. This new technique eliminated the limitation of the application with the traditional passive resistors for low frequency applications. Based on the understanding of this relationship, a strategy for designing high linear analog continuous-time filters has been developed. According to our strategy, a prototype analog integrated filter has been designed and fabricated with AMI05 0.5 um standard CMOS process. Experimental results proved this technique has the ability to provide excellent linearity with very limited active area.;In Chapter 2, the relationships between the transconductance networks and major circuit specifications have been explored. The analysis reveals the trade off between the silicon area saved by the transconductance networks and the some other important specifications such as linearity, noise level and the process variations of the overall circuit. Experimental results of discrete component circuit matched very well with our analytical outcomes to predict the change of linearity and noise performance associated with different transconductance networks.;The Chapter 3 contains the analysis and mathematical proves of the optimum passive area allocations for several most popular analog active filters. Because the total area is now manageable by the technique introduced in the Chapter 1, the further reduce of the total area will be very important and useful for efficient utilizing the silicon area, especially with the today\u27s fast growing area efficiency of the highly density digital circuits. This study presents the mathematical conclusion that the minimum passive area will be achieved with the equalized resistor and capacitor.;In the Chapter 4, a well recognized and highly honored current division circuit has been studied. Although it was claimed to be inherently linear and there are over 60 published works reported with high linearity based on this technique, our study discovered that this current division circuit can achieve, if proper circuit condition being managed, very limited linearity and all the experimental verified performance actually based on more general circuit principle. Besides its limitation, however, we invented a novel current division digital to analog converter (DAC) based on this technique. Benefiting from the simple circuit structure and moderate good linearity, a prototype 8-bit DAC was designed in TSMC018 0.2 um CMOS process and the post layout simulations exhibited the good linearity with very low power consumption and extreme small active area.;As the part of study of the output stage for the current division DAC discussed in the Chapter 4, a current mirror is expected to amplify the output current to drive the low resistive load. The strategy of achieving the optimum bandwidth of the cascode current mirror with fixed total current gain is discussed in the Chapter 5.;Improving the linearity of pipeline ADC has been the hottest and hardest topic in solid-state circuit community for decade. In the Chapter 6, a comprehensive study focus on the existing calibration algorithms for pipeline ADCs is presented. The benefits and limitations of different calibration algorithms have been discussed. Based on the understanding of those reported works, a new model-based calibration is delivered. The simulation results demonstrate that the model-based algorithms are vulnerable to the model accuracy and this weakness is very hard to be removed. From there, we predict the future developments of calibration algorithms that can break the linearity limitations for pipelined ADC. (Abstract shortened by UMI.

A framework for fine-grain synthesis optimization of operational amplifiers

This thesis presents a cell-level framework for Operational Amplifiers Synthesis (OASYN) coupling both circuit design and layout. For circuit design, the tool applies a corner-driven optimization, accounting for on-chip performance variations. By exploring the process, voltage, and temperature variations space, the tool extracts design worst case solution. The tool undergoes sensitivity analysis along with Pareto-optimality to achieve required specifications. For layout phase, OASYN generates a DRC proved automated layout based on a sized circuit-level description. Morata et al. (1996) introduced an elegant representation of block placement called sequence pair for general floorplans (SP). Like TCG and BSG, but unlike O-tree, B*tree, and CBL, SP is P-admissible. Unlike SP, TCG supports incremental update during operation and keeps the information of the boundary modules as well as their relative positions in the representation. Block placement algorithms that are based on SP use heuristic optimization algorithms, e.g., simulated annealing where generation of large number of sequence pairs are required. Therefore a fast algorithm is needed to generate sequence pairs after each solution perturbation. The thesis presents a new simple and efficient O(n) runtime algorithm for fast realization of incremental update for cost evaluation. The algorithm integrates sequence pair and transitive closure graph advantages into TCG-S* a superior topology update scheme which facilitates the search for optimum desired floorplan. Experiments show that TCG-S* is better than existing works in terms of area utilization and convergence speed. Routing-aware placement is implemented in OASYN, handling symmetry constraints, e.g., interdigitization, common centroid, along with congestion elimination and the enhancement of placement routability

Novel structures for high-speed delta-sigma data converters

As CMOS processes keep scaling down devices, the maximum operating frequencies of CMOS devices increase, and hence circuits can process very wide band signals. Moreover, the small physical dimensions of transistors allow the placing of many more blocks into a single chip, including highly accurate analog blocks and complicated digital blocks, which can process audio to communication data. Nowadays, wideband and low-power data converter is mandatory for mobile applications which need a bridge between analog and digital blocks. In this dissertation, low-power and wideband techniques are proposed. An embedded-adder quantizer with dynamic preamplifier is proposed to achieve power-efficient operation. Various double-sampling schemes are studied, and novel schemes are presented to achieve wideband operation without noise folding effect. To reduce timing delay and idle tones, a high speed DEM which alternates two sets of comparator references is proposed. Multi-cell architecture is studied to insure higher performance when the number of modulators increases. 0.18 um double-poly/4-metal CMOS process was used to implement a prototype IC. 20 MHz signal bandwidth was achieved with a 320 MHz sampling clock. The peak SNDR was 63 dB. The figure-of-merit FoM = P/(2*BW*2[superscript ENOB]) was 0.35 pJ/conversion, with a 16 mW power consumption. Measurement results show that the proposed design ideas are useful for low-power and wideband delta-sigma modulators which have low OSR. A second-order noise-coupled modulator with an embedded-zero optimization was proposed to reduce power consumption by eliminating some of the integrators. This architecture makes easier the implementation of the small feedback capacitors for high OSR modulators

Low power high speed and high accuracy design methodologies for pipeline Analog-to-Digital converters

Different aspects of power optimization of a high-speed, high-accuracy pipeline Analog-to-Digital Converters (ADCs) are considered to satisfy the current and future needs of portable communication devices. First power optimized design strategies for the amplifiers are introduced. Closed form expressions of power w.r.t settling requirements are presented to facilitate a fair comparison and selection of the amplifier structure. Next a new low offset dynamic comparator has been designed. Simulation based sensitivity analysis is performed to demonstrate the robustness of the new comparator with respect to stray capacitances, common mode voltage errors and timing errors. With simplified amplifier power model along with the use of dynamic comparators, a method to optimize the power consumption of a pipeline ADC with kT/C noise constraint is also developed. The total power dependence on capacitor scaling and stage resolution is investigated for a near-optimal solution.;After considering the power requirements of a pipeline ADC, design and statistical modeling of over-range protection requirements is investigated. Closed form statistical expressions for the over-range requirements are developed to assist in the allocation of the error budgets to different pipeline blocks. A new over-range protection algorithm is also developed that relaxes the amplifier design and power requirements.;Finally, two new CMOS Schmitt trigger designs are proposed which can be used as clock inputs for the pipeline ADC. In the new designs, sizing of the feedback inverters is used for independent trip point control. The new designs have also a modest reduction in sensitivity to process variations along with immunity to the kick-back noise without the addition of path delay

Mixed-signal integrated circuits design and validation for automotive electronics applications

Automotive electronics is a fast growing market. In a field primarily dominated by mechanical or hydraulic systems, over the past few decades there has been exponential growth in the number of electronic components incorporated into automobiles. Partly thanks to the advance in high voltage smart power processes in nowadays cars is possible to integrate both power/high voltage electronics and analog/digital signal processing circuitry thus allowing to replace a lot of mechanical systems with electro-mechanical or fully electronic ones. High level modeling of complex electronic systems is gaining importance relatively to design space exploration, enabling shorter design and verification cycles, allowing reduced time-to-market. A high level model of a resistor string DAC to evaluate nonlinearities has been developed in MATLAB environment. As a test case for the model, a 10 bit resistive DAC in 0.18um is designed and the results were compared with the traditional transistor level approach. Then we face the analysis and design of a fundamental block: the bandgap voltage reference. Automotive requirements are tough, so the design of the voltage reference includes a pre-regulation part of the battery voltage that allows to enhance overall performances. Moreover an analog integrated driver for an automotive application whose architecture exploits today’s trends of analog-digital integration allowing a greater range of flexibility allowing high configurability and fast prototipization is presented. We covered also the mixed-signal verification approach. In fact, as complexity increases and mixed-signal systems become more and more pervasive, test and verification often tend to be the bottleneck in terms of time effort. A complete flow for mixed-signal verification using VHDL-AMS modeling and Python scripting is presented as an alternative to complex transistor level simulations. Finally conclusions are drawn

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

Statistical Classification Based Modelling and Estimation of Analog Circuits Failure Probability

At nanoscales, variations in transistor parameters cause variations and unpredictability in the circuit output, and may ultimately cause a violation of the desired specifications, leading to circuit failure. The parametric variations in transistors occur due to limitations in the manufacturing process and are commonly known as process variations. Circuit simulation is a Computer-Aided Design (CAD) technique for verifying the behavior of analog circuits but exhibits incompleteness under the effects of process variations. Hence, statistical circuit simulation is showing increasing importance for circuit design to address this incompleteness problem. However, existing statistical circuit simulation approaches either fail to analyze the rare failure events accurately and efficiently or are impractical to use. Moreover, none of the existing approaches is able to successfully analyze analog circuits in the presence of multiple performance specifications in timely and accurate manner. Therefore, we propose a new statistical circuit simulation based methodology for modelling and estimation of failure probability of analog circuits in the presence of multiple performance metrics. Our methodology is based on an iterative way of estimating failure probability, employing a statistical classifier to reduce the number of simulations while still maintaining high estimation accuracy. Furthermore, a more practical classifier model is proposed for analog circuit failure probability estimation. Our methodology estimates an accurate failure probability even when the failures resulting from each performance metric occur simultaneously. The proposed methodology can deliver many orders of speedup compared to traditional Monte Carlo methods. Moreover, experimental results show that the methodology generates accurate results for problems with multiple specifications, while other approaches fail totally

Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field