Digital Centric Multi-Gigabit SerDes Design and Verification

Advances in semiconductor manufacturing still lead to ever decreasing feature sizes and constantly allow higher degrees of integration in application specific integrated circuits (ASICs). Therefore the bandwidth requirements on the external interfaces of such systems on chips (SoC) are steadily growing. Yet, as the number of pins on these ASICs is not increasing in the same pace - known as pin limitation - the bandwidth per pin has to be increased. SerDes (Serializer/Deserializer) technology, which allows to transfer data serially at very high data rates of 25Gbps and more is a key technology to overcome pin limitation and exploit the computing power that can be achieved in todays SoCs. As such SerDes blocks together with the digital logic interfacing them form complex mixed signal systems, verification of performance and functional correctness is very challenging. In this thesis a novel mixed-signal design methodology is proposed, which tightly couples model and implementation in order to ensure consistency throughout the design cycles and hereby accelerate the overall implementation flow. A tool flow that has been developed is presented, which integrates well into state of the art electronic design automation (EDA) environments and enables the usage of this methodology in practice. Further, the design space of todays high-speed serial links is analyzed and an architecture is proposed, which pushes complexity into the digital domain in order to achieve robustness, portability between manufacturing processes and scaling with advanced node technologies. The all digital phase locked loop (PLL) and clock data recovery (CDR), which have been developed are described in detail. The developed design flow was used for the implementation of the SerDes architecture in a 28nm silicon process and proved to be indispensable for future projects

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

Fault-based Analysis of Industrial Cyber-Physical Systems

The fourth industrial revolution called Industry 4.0 tries to bridge the gap between traditional Electronic Design Automation (EDA) technologies and the necessity of innovating in many indus- trial fields, e.g., automotive, avionic, and manufacturing. This complex digitalization process in- volves every industrial facility and comprises the transformation of methodologies, techniques, and tools to improve the efficiency of every industrial process. The enhancement of functional safety in Industry 4.0 applications needs to exploit the studies related to model-based and data-driven anal- yses of the deployed Industrial Cyber-Physical System (ICPS). Modeling an ICPS is possible at different abstraction levels, relying on the physical details included in the model and necessary to describe specific system behaviors. However, it is extremely complicated because an ICPS is com- posed of heterogeneous components related to different physical domains, e.g., digital, electrical, and mechanical. In addition, it is also necessary to consider not only nominal behaviors but even faulty behaviors to perform more specific analyses, e.g., predictive maintenance of specific assets. Nevertheless, these faulty data are usually not present or not available directly from the industrial machinery. To overcome these limitations, constructing a virtual model of an ICPS extended with different classes of faults enables the characterization of faulty behaviors of the system influenced by different faults. In literature, these topics are addressed with non-uniformly approaches and with the absence of standardized and automatic methodologies for describing and simulating faults in the different domains composing an ICPS. This thesis attempts to overcome these state-of-the-art gaps by proposing novel methodologies, techniques, and tools to: model and simulate analog and multi-domain systems; abstract low-level models to higher-level behavioral models; and monitor industrial systems based on the Industrial Internet of Things (IIOT) paradigm. Specifically, the proposed contributions involve the exten- sion of state-of-the-art fault injection practices to improve the ICPSs safety, the development of frameworks for safety operations automatization, and the definition of a monitoring framework for ICPSs. Overall, fault injection in analog and digital models is the state of the practice to en- sure functional safety, as mentioned in the ISO 26262 standard specific for the automotive field. Starting from state-of-the-art defects defined for analog descriptions, new defects are proposed to enhance the IEEE P2427 draft standard for analog defect modeling and coverage. Moreover, dif- ferent techniques to abstract a transistor-level model to a behavioral model are proposed to speed up the simulation of faulty circuits. Therefore, unlike the electrical domain, there is no extensive use of fault injection techniques in the mechanical one. Thus, extending the fault injection to the mechanical and thermal fields allows for supporting the definition and evaluation of more reliable safety mechanisms. Hence, a taxonomy of mechanical faults is derived from the electrical domain by exploiting the physical analogies. Furthermore, specific tools are built for automatically instru- menting different descriptions with multi-domain faults. The entire work is proposed as a basis for supporting the creation of increasingly resilient and secure ICPS that need to preserve functional safety in any operating context

Modelagem de um PLL e projeto de VCO para transceptor ZigBee

Monografia (graduação)—Universidade de Brasília, Faculdade UnB Gama, Curso de Engenharia Eletrônica, 2015.A fim de estabelecer redes de comunicação sem fio (WSN) que agreguem baixo custo, baixo consumo, baixa taxa de transmissão de dados, confiabilidade e segurança, o protocolo ZigBee (IEEE 802.15.4) aparece como uma excelente alternativa, podendo ser aplicado em diversos contextos e finalidades, por exemplo em Smart Grids. O objetivo deste trabalho é a modelagem de um Phase Locked Loop (PLL) e projeto de um Oscilador Controlado por Tensão (VCO) para transceptor ZigBee. O projeto foi desenvolvido com auxílio das ferramentas Cadence seguindo a metodologia de projetos Top-Down. O Verilog-AMS foi a linguagem de descrição de hardware utilizada na modelagem, a qual possibilita a análise comportamental e simulação mista. Este trabalho foi dividido em duas partes: fundamentos teóricos (feito no TCC1) e implementação, realizada na disciplina TCC2. Na primeira parte, o levantamento inicial das especificações dos circuitos foram apresentados, resultando na proposta da topologia do PLL e em possíveis topologias para o VCO tanque LC. Na segunda parte, o sistema PLL foi completamente modelado usando Verilog-AMS e o VCO foi projetado em nível transistor, utilizando a tecnologia CMOS TSMC 0.18um. Os outros blocos que compõem o sistema PLL foram desenvolvidos em nível transistor por outros alunos. Em paralelo com o trabalho de modelagem, simulações mistas, utilizando os circuitos desenvolvidos neste trabalho bem como os circuitos desenvolvidos pelos outros alunos, foram realizadas mostrando resultados promissores. ________________________________________________________________________________ ABSTRACTIn order to establish Wireless Sensor Networks (WSN) which aggregate low cost, low power, low data rate, reliability and security, the ZigBee protocol (IEEE 802.15.4) appears as an excellent alternative and can be applied to many different contexts and proposes, e.g. Smart Grids. The objective of this work is to model a Phase Locked Loop (PLL) and designing a Voltage Controlled Oscillator (VCO) for a ZigBee transceiver. The project was developed using the Cadence tools following the Top-Down methodology for integrated circuit designs. The Verilog-AMS was the main hardware description language used in the modeling, making possible the behavioral analysis and mixed signal simulations. This academic work was divided in two parts: the theoretical foundation (done in the TCC1 discipline) and the implementation, accomplished in the TCC2 discipline. An initial survey of the circuits specifications was also done, resulting in a proposal of topologies for the PLL and the VCO LC-tank. In the TCC2 discipline, the PLL system was completely modeled using Verilog-AMS and the VCO was designed in transistor level, using the TSMC 0.18um CMOS technology. The other blocks that compound the PLL system were developed in transistor level by other students. In parallel with the modeling work, mixed signal simulations, using the circuits developed in this work as well as the circuits developed by other students, were carried out showing promising results

Algorithms for Verification of Analog and Mixed-Signal Integrated Circuits

Over the past few decades, the tremendous growth in the complexity of analog and mixed-signal (AMS) systems has posed great challenges to AMS verification, resulting in a rapidly growing verification gap. Existing formal methods provide appealing completeness and reliability, yet they suffer from their limited efficiency and scalability. Data oriented machine learning based methods offer efficient and scalable solutions but do not guarantee completeness or full coverage. Additionally, the trend towards shorter time to market for AMS chips urges the development of efficient verification algorithms to accelerate with the joint design and testing phases. This dissertation envisions a hierarchical and hybrid AMS verification framework by consolidating assorted algorithms to embrace efficiency, scalability and completeness in a statistical sense. Leveraging diverse advantages from various verification techniques, this dissertation develops algorithms in different categories. In the context of formal methods, this dissertation proposes a generic and comprehensive model abstraction paradigm to model AMS content with a unifying analog representation. Moreover, an algorithm is proposed to parallelize reachability analysis by decomposing AMS systems into subsystems with lower complexity, and dividing the circuit's reachable state space exploration, which is formulated as a satisfiability problem, into subproblems with a reduced number of constraints. The proposed modeling method and the hierarchical parallelization enhance the efficiency and scalability of reachability analysis for AMS verification. On the subject of learning based method, the dissertation proposes to convert the verification problem into a binary classification problem solved using support vector machine (SVM) based learning algorithms. To reduce the need of simulations for training sample collection, an active learning strategy based on probabilistic version space reduction is proposed to perform adaptive sampling. An expansion of the active learning strategy for the purpose of conservative prediction is leveraged to minimize the occurrence of false negatives. Moreover, another learning based method is proposed to characterize AMS systems with a sparse Bayesian learning regression model. An implicit feature weighting mechanism based on the kernel method is embedded in the Bayesian learning model for concurrent quantification of influence of circuit parameters on the targeted specification, which can be efficiently solved in an iterative method similar to the expectation maximization (EM) algorithm. Besides, the achieved sparse parameter weighting offers favorable assistance to design analysis and test optimization