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SystematIC Design has a number of publications on it's name. A short description is given below. All theses were published by the Delft University Press in The Netherlands.
Integrated Smart Sensor: Design and Calibration The production costs for sensors can be reduced significantly by automating the calibration procedure. This book presents different options for including a digitally programmable calibration circuit in an integrated smart sensor. These options are based on a calibration of the signal transfer using analog signal processing, digital signal processing, or a mixed-mode technique using sigma-delta modulation. Besides these calibration circuits, also the other functional blocks needed in integrated smart sensors are presented. Furthermore, several linearization techniques are explained, including an efficient step-wise polynomial calibration method. The combination of a programmable calibration circuit and a digital bus interface, both included in the smart sensor, enables the desired automation of the calibration procedure for a large batch of sensors at a time.
Integrated Smart Sensors: DESIGN and CALIBRATION
Design of Low-Voltage Integrated Filter-Mixer Systems A systematic and hierarchical design method for active continuous-time filters is described in Chapter 2. The method presented is focused on the design of filters that can be directly described by the state-space equations. This does not limit the filter performance, only the number of possible filter structures is limited to achieve better designability. Following this introduction, filters are considered with respect to the three fundamental limitations in signal processing: noise, distortion, and bandwidth. Chapter 3 describes the noise and distortion for single filters, but also for filters that are coupled by mixers. Mixers can be used for transforming signals to lower frequencies in the spectrum, allowing for filters with lower quality factors (Q-factors). This results in an overall higher dynamic range. Finally, the limits of state-space filters are reconsidered. Other integrator structures and topologies are also considered, achieving a higher dynamic range at the cost of an increased current consumption. Bandwidth limitations are due to parasitics in both active and passive elements. Chapter 4 shows some methods for compensating the non-ideal frequency behavior of the active part at various hierarchical levels of the filter design trajectory. Non-linearities limit the maximum output signal of filters. Clipping is mostly seen as the upper limit that determines the dynamic range of the filter signal. However, before clipping occurs, weak deviations from the linear transfer function are already present. A more suitable measure for the dynamic range (harmonic free dynamic range) is presented in Chapter 5, together with the appropriate scaling criteria. In Chapter 6 a physical model of the JFET is derived that can be used for the prediction of the non-linearities of the JFET. The model is derived and corrected on the basis of very accurate measurements. The JFET appears to be very useful in low-voltage applications. A radio receiver operating at 1 Volt is presented in Chapter 7. The kernel of the receiver is an active continuous-time filter that has been designed according to the insights gained from the preceding chapters. It appears possible to design a single-chip long-wave receiver, and even a medium-wave receiver.
Design of Low-Voltage Integrated Filter-Mixer Systems
The Identification of Analytical Device Models Analytical device models play an important role in the design and analysis of electronic circuits. The high level of abstraction of the analytical models is essential for the formulation of circuit design theory. At the same time analytical models remain closely linked to the physical structure of the devices they represent, which means that their parameters can be related to the process steps and the device geometry. This makes analytical device models the natural choice for applications such as process characterization and the design of integrated circuits and devices. The accuracy with which a device model can predict the actual behavior of a device depends on the proper definition of the model parameters. In this thesis we present a unified approach to the identification of analytical device models, which has resulted in the MODES has the flexibility of a data-fitting method, and can be applied without any modification to analytical models of arbitrary complexity. In addition, the validity domain of the model is extracted automatically from the observed device behavior, using a well-defined validity criterion. Supplying the validity domain exposes and localizes the model's deficiencies, showing where the model needs to be extended. This feature makes MODES a particularly useful tool for the development of new models and the improvement of existing models. Because MODES isolates the deficiencies of the model, the model parameters generated with MODES will improve the accuracy of circuit simulations, and so improve the reliability of the designed circuits. The implementation of MODES presented in this thesis combines robustness with efficiency. Compared to conventional data-fitting algorithms, our algorithm is marked by its proficient handling of strongly non-linear models and its ability to deal effectively with over-parameterized models. As these contributions can improve the robustness of data-fitting algorithms in general, they should be of interest to a wider audience. Finally, the application of the MODES identification method is not limited to the modeling of electronic devices. MODES only relies on the asymptotic character of the model, an assumption which is justifiable for most analytical models in other branches of science and engineering.
The Identification of Analytical Device Models
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