Real time imaging radar requires short dwell time and low hardware complexity, limiting the application of many existing radar modulation methods. In this paper, we propose a novel Successive Multiple Frequency Continuous Wave (S-MFCW) modulation scheme to address these challenges. The S-MFCW signal model is formulated and the impact of noise is analyzed, leading to a very short dwell time and narrow signal bandwidth with excellent range and speed accuracy. From the numerical simulation of an automotive radar example, we show that S-MFCW achieves comparable range and speed performance, but orders of magnitude lower requirements on dwell time and bandwidth compared to the commonly used FMCW radar.

High quality factor configurable inductors are critical components for robust multiband wireless applications. Configurable Multilayer Coplanar Wave Guide (MCPW) based inductors in nano-scale CMOS technologies are proposed in this paper for millimeter wave frequency applications. Inductors are tweaked by adjusting the transmission line length to compensate for process variations of the mm-wave circuits and cover multiple bands. For demonstration, a mm-wave Voltage Controlled Oscillator (VCO) using the configurable inductor was designed and fabricated. Measurement reveals that the VCO covers frequency ranging from 72.8 to 77.6 GHz with phase noise of -109 dBc/Hz at 10 MHz offset, which is in good agreement with the MCPW-based inductor model prediction.

Long design cycles due to the inability to predict silicon realities are a well-known problem that plagues analog/RF integrated circuit product development. As this problem worsens for nanoscale IC technologies, the high cost of design and multiple manufacturing spins causes fewer products to have the volume required to support full-custom implementation. Design reuse and analog synthesis make analog/RF design more affordable; however, the increasing process variability and lack of modeling accuracy remain extremely challenging for nanoscale analog/RF design. We propose a regular analog/RF IC using metal-mask configurability design methodology Optimization with Recourse of Analog Circuits including Layout Extraction (ORACLE), which is a combination of reuse and shared-use by formulating the synthesis problem as an optimization with recourse problem. Using a two-stage geometric programming with recourse approach, ORACLE solves for both the globally optimal shared and application-specific variables. Furthermore, robust optimization is proposed to treat the design with variability problem, further enhancing the ORACLE methodology by providing yield bound for each configuration of regular designs. The statistical variations of the process parameters are captured by a confidence ellipsoid. We demonstrate ORACLE for regular Low Noise Amplifier designs using metal-mask configurability, where a range of applications share common underlying structure and application-specific customization is performed using the metal-mask layers. Two RF oscillator design examples are shown to achieve robust designs with guaranteed yield bound.

In this paper, a robust analog design (ROAD) tool for post-tuning (i.e., locally optimizing) analog/RF circuits is proposed. Starting from an initial design derived from hand analysis or analog circuit optimization based on simplified models, ROAD extracts accurate performance models via transistor-level simulation and iteratively improves the circuit performance by a sequence of geometric programming steps. Importantly, ROAD sets up all design constraints to include large-scale process and environmental variations, thereby facilitating the tradeoff between yield and performance. A crucial component of ROAD is a novel projection-based scheme for quadratic (both polynomial and posynomial) performance modeling, which allows our approach to scale well to large problem sizes. A key feature of this projection-based scheme is a new implicit power iteration algorithm to find the optimal projection space and extract the unknown model coefficients with robust convergence. The efficacy of ROAD is demonstrated on several circuit examples

As the design-manufacturing interface becomes increasingly complicated with IC technology scaling, the corresponding process variability poses great challenges for nanoscale analog/RF design. Design optimization based on the enumeration of process corners has been widely used , but can suffer from inefficiency and overdesign. In this paper we propose to formulate the analog and RF design with variability problem as a special type of robust optimization problem, namely robust geometric programming. The statistical variations in both the process parameters and design variables are captured by a pre-specified confidence ellipsoid. Using such optimization with ellipsoidal uncertainy approach, robust design can be obtained with guaranteed yield bound and lower design cost, and most importantly, the problem size grows linearly with number of uncertain parameters. Numerical examples demonstrate the efficiency and reveal the trade-off between the design cost versus the yield requirement. We will also demonstrate significant improvement in the design cost using this approach compared with corner-enumeration optimization.

The increasing complexity of today's mixed-signal integrated circuits necessitates both top-down and bottom-up system-level verification. Time-domain state-space modeling and simulation approaches have been successfully applied for such purposes (e.g. Simulink); however, analog circuits are often best analyzed in the frequency domain. Circuit-level analyses, such as harmonic balance, have been successfully extended to the frequency domain [2], but these algorithms are impractical for simulating large systems with wide-band input and noise signals. In this paper we proposed a frequency-domain approach for analog/RF system-level simulation that is capable of capturing various second order effects (e.g. nonlinearity, noise, etc.) for both time-invariant and time-varying systems with wide-band inputs. The simulator directly evaluates the frequency domain response at each node via a relaxation scheme that is proven to be convergent under typical circuit conditions. Our experimental results demonstrate the accuracy and efficiency of the proposed simulator under various wide-band input and noise excitations.

Long design cycles due to the inability to predict silicon realities is a well-known problem that plagues analog/RF integrated circuit product development. As this problem worsens for technologies below 100nm, the high cost of design and multiple manufacturing spins causes fewer products to have the volume required to support full custom implementation. Design reuse and analog synthesis make analog/RF design more affordable; however, the increasing process variability and lack of modeling accuracy remains extremely challenging for nanoscale analog/RF design. We propose an analog/RF circuit design methodology ORACLE, which is a combination of reuse and \emph{shared-use by formulating the synthesis problem as an \emph{optimization with recourse problem. Using a two-stage geometric programming with recourse approach, ORACLE solves for both the globally optimal shared and application-specific variables. Concurrently, we demonstrate ORACLE for novel metal-mask configurable designs, where a range of applications share common underlying structure and application-specific customization is performed using the metal-mask layers. We also include the silicon validation of the metal-mask configurable designs.

We propose a robust analog design tool (ROAD) for post-tuning analog/RF circuits. Starting from an initial design derived from hand analysis or analog circuit synthesis based on simplified models, ROAD extracts accurate posynomial performance models via transistor-level simulation and optimizes the circuit by geometric programming. Importantly, ROAD sets up all design constraints to include large-scale process variations to facilitate the tradeoff between yield and performance. A novel convex formulation of the robust design problem is utilized to improve the optimization efficiency and to produce a solution that is superior to other local tuning methods. In addition, a novel projection-based approach for posynomial fitting is used to facilitate scaling to large problem sizes. A new implicit power iteration algorithm is proposed to find the optimal projection space and extract the posynomial coefficients with robust convergence. The efficacy of ROAD is demonstrated on several circuit examples.

In this paper we propose a frequency-separation methodology to generate system-level macromodels for analog and RF circuits. The proposed macromodels are similar in form to those based on Volterra kernel calculations, but are much simpler in terms of characterization and overall model complexity, and can be derived from existing device models. This simplicity is realized by applying some basic assumptions on the form of the input excitations, and via separation of the nonlinearities from the dynamic behavior. In addition, by further separating the ideal model functionality, this macromodel is applicable to strongly nonlinear components such as mixers. While time-varying Volterra series models have been proposed for mixers with a fixed local oscillation (LO) signal, the proposed frequency separation model is completely general and can capture the variations of the LO input during a system-level simulation. The proposed macromodels are demonstrated in a system-level simulation tool based on Simulink for efficient evaluation of the entire RF system and associated components. A GSM receiver system in 0.25μm CMOS process is used to demonstrate the efficacy of these macromodels in our system-level simulation environment.

Design and validation of mixed-signal integrated systems require system-level model abstractions. Generalized Volterra series based models have been successfully applied for analog and RF component macromodels, but their complexity can sometimes limit their utility for time-varying systems and large circuits with complex device models or numerous parasitics. In this paper we propose simple and efficient analog and RF circuit macromodels that provide accurate model abstractions for large, complex time-varying circuits over frequency bands of interest. By starting with the system-level block diagram model structures and focusing on the narrow RF bands, the proposed macromodels can efficiently capture the nonlinear behavior as well as the impact of RLC coupling parasitics via compact reduced-order model forms. While the macromodel can trade accuracy for simplicity in terms of the number of frequency expansion points, we find that expansion about one frequency point provides the accuracy required for system-level analysis of most RF and narrow-band analog components. The macromodel form corresponds to block diagram structures that are easily incorporated into our system-level simulation tool based on Simulink.

Noise performance is a critical analog and RF circuit design constraint, and can impact the selection of the IC system-level architecture. It is therefore imperative that some model of the noise is represented at the highest levels of abstraction during the design process. In this paper we propose a noise macromodel for analog circuits and demonstrate it by way of implementation in a system level simulator based on MATLAB. We also explain our process of macromodel extraction via reformulation of frequency-domain noise analysis results, and the corresponding steps of model order reduction. The results demonstrate the efficacy of this macromodel for frequency domain system level simulation.