Data converters are a particularly important class of mixed-signal circuit. Their performance improves with time, driven by market forces leading to developments in design and manufacturing processes. Device testing, however, is becoming a bottleneck as available tester resources are limited in terms of accuracy when test time is limited. Model-based testing has been proposed to address this problem. In chapter 2 of this thesis, we demonstrate model-based testing for a 12-bit DAC, following an algorithm originally proposed by the National Institute of Standards and Technology (NIST), which marks the starting point of the work. We identify shortcomings with the NIST algorithm and open questions that are addressed in the remainder of the thesis. Based on the experience with DAC modeling, we apply our refined modeling procedures to a 12-bit ADC in Chapter 3. For this device, we demonstrate advantages and discuss trade-offs when model-based testing is applied in a production test environment. In a trial run, testing two wafer lots of devices, the robustness of the model is demonstrated in the presence of significant manufacturing process drifts. The choice of test conditions is discussed in Chapter 4. Experimental evidence is given that a test condition which yields more precise model parameter estimates is advantageous. In the example, the same condition is used for another test performed on the devices; thus, measurements can be shared between both tests. The idea is taken further towards Design-for-Testability considerations and towards Design-for-Test, reducing measurement accuracy limitations that are encountered for high-resolution data converters. In Chapter 5, test point selection strategies are discussed. In particular, structural faults are considered, and a novel test point selection algorithm is developed to cover hard-faults. This algorithm yields a single set of test points that provides hard-fault coverage as well as serving the model-based device parameter extraction that is a cornerstone of the test effort reduction technique based on linear modeling. In Chapter 6, we apply the modeling techniques developed previously to characterize the influence of the manufacturing process on device performance. In this application, we exploit design information that was used to form the a priori model. Certain types of design changes can be accommodated in this model and the performance of a re-design can be predicted. We consider re-sizing of circuit elements and digital calibration techniques as examples of possible re-designs.

The Analogue-to-digital converter (ADC) is the most pervasive block in electronic systems. With the advent of powerful digital signal processing and digital communication techniques, ADCs are fast becoming critical components for system’s performance and flexibility. Knowing accurately all the parameters that characterise their dynamic behaviour is crucial, on one hand to select the most adequate ADC architecture and characteristics for each end application, and on the other hand, to understand how they affect performance bottlenecks in the signal processing chain. Dynamic Characterisation of Analogue-to-Digital Converters presents a state of the art overview of the methods and procedures employed for characterising ADCs’ dynamic performance behaviour using sinusoidal stimuli. The three classical methods – histogram, sine wave fitting, and spectral analysis – are thoroughly described, and new approaches are proposed to circumvent some of their limitations. This is a must-have compendium, which can be used by both academics and test professionals to understand the fundamental mathematics underlining the algorithms of ADC testing, and as an handbook to help the engineer in the most important and critical details for their implementation.

Testing and characterizing of Analog to Digital Converter (ADC) is still a challenging concern for real time mixed signal analysis, manufacturers and designers for consideration of factor like cost and time. The goal of such process is to validate in a short time whether a given ADC unite its performance requirements. ADC is an important device generally used in today's advanced communication and electronics applications like: microwave system, military applications, satellite communication and medical application for interfacing analog electronics with digital electronics. The ADC testing is mainly resolute by three technologies: linear stimulus generation, fast data capture and precision clock timing. The bottle neck analyzed in testing of recently high-performance ADCs is the linear signal generation, as the present need of technologies on timing and data capture can handle the testing need of upcoming ADCs. For current high-resolution ADCs, time for full-code INL and DNL test, which is directly related with the cost, is comparative long because of the large number of variables to be accurately measured.

This book is the first graduate-level textbook presenting a comprehensive treatment of Data Converters. The advancement of digital electronics urged the availability of a still missing support for teaching and self-learning analog-digital interfaces at many levels: the specification, the conversion methods and architectures, the circuit design and the testing. This book, after the necessary study of the background theoretical elements, covers aspects and provide elements for a deep and comprehensive knowledge. The breath and the level of details of topics is enhanced by introductory material in each chapter and the use of many examples, most of them in the form of computer behavioral simulations. The examples and the end-of-chapter problems help in understanding and favor self-practice using tools that are effective for training and for design activity. Data Converters is a textbook that is also essential for engineering professionals as it was written for responding to a shortage of organically organized material on the topic. The book assumes a solid background in analog and digital circuits as well as a working knowledge of simulation tools for circuit and behavioral analysis. A background on statistical analysis is also helpful, though not strictly necessary. Coverage of all the basic elements essential for a clear understanding of sampling, quantization, noise in sampled-data systems and mathematical tools for sampled-data linear systems Comprehensive definition of the parameters used to specify data converters and necessary for understanding product data sheets Coverage of all the architectures used in Nyquist-rate data converters and detailed study of features, limits and design techniques Detailed study of oversampled and Sigma-Delta converters with simulation examples and use of spectra and histograms for a clear understanding of features and limit if the noise shaping Coverage of digital correction and calibration techniques for enhancing performances Use of theory and intuitive views to explain circuits and systems operation and limits Coverage of testing methods and description of the data processing used for testing and characterization Extensive use of Simulink and Matlab in examples and problem sets to assist reader comprehension and favor deeper study

The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.