This paper presents a high-level synthesis tool for /spl Sigma//spl Delta/ modulators (/spl Sigma//spl Delta/Ms) that combines an accurate SIMULINK-based time-domain behavioral simulator with a statistical optimization core. Three different circuit techniques for the modulator implementation are considered: switched-capacitor, switched-current and continuous-time. The behavioral models of these circuits, that take into account the most critical limiting factors, have been incorporated into the SIMULINK environment by using S-function blocks, which drastically increase the computational efficiency. The precision of these models has been validated by electrical simulations using HSPICE and experimental measurements from several silicon prototypes. The combination of high accuracy, short CPU time and interoperability of different circuit models together with the efficiency of the optimization engine makes the proposed tool an advantageous alternative for /spl Sigma//spl Delta/M synthesis. The implementation on the well-known MATLAB/SIMULINK platform brings numerous advantages in terms of data manipulation, processing capabilities, flexibility and simulation with other electronic subsystems. Moreover, this is the first tool dealing with the synthesis of /spl Sigma//spl Delta/Ms using both discrete-time and continuous-time circuit techniques.

In nanometer complementary metal-oxide-semiconductor technologies, worst-case design methods and response-surface-based yield optimization methods face challenges in accuracy. Monte-Carlo (MC) simulation is general and accurate for yield estimation, but its efficiency is not high enough to make MC-based analog yield optimization, which requires many yield estimations, practical. In this paper, techniques inspired by computational intelligence are used to speed up yield optimization without sacrificing accuracy. A new sampling-based yield optimization approach, which determines the device sizes to optimize yield, is presented, called the ordinal optimization (OO)-based random-scale differential evolution (ORDE) algorithm. By proposing a two-stage estimation flow and introducing the OO technique in the first stage, sufficient samples are allocated to promising solutions, and repeated MC simulations of non-critical solutions are avoided. By the proposed evolutionary algorithm that uses differential evolution for global search and a random-scale mutation operator for fine tunings, the convergence speed of the yield optimization can be enhanced significantly. With the same accuracy, the resulting ORDE algorithm can achieve approximately a tenfold improvement in computational effort compared to an improved MC-based yield optimization algorithm integrating the infeasible sampling and Latin-hypercube sampling techniques. Furthermore, ORDE is extended from plain yield optimization to process-variation-aware single-objective circuit sizing.

This paper proposes new pathological element-based active device models which can be used in analysis tasks of linear(ized) analog circuits. Nullators and norators along with the voltage mirror-current mirror (VM-CM) pair (collectively known as pathological elements) are used to model the behavior of active devices in voltage-, current-, and mixed-mode, also considering parasitic elements. Since analog circuits are transformed to nullor-based equivalent circuits or VM-CM pairs or as a combination of both, standard nodal analysis can be used to formulate the admittance matrix. We present a formulation method in order to build the nodal admittance (NA) matrix of nullor-equivalent circuits, where the order of the matrix is given by the number of nodes minus the number of nullors. Since pathological elements are used to model the behavior of active devices, we introduce a more efficient formulation method in order to compute small-signal characteristics of pathological element-based equivalent circuits, where the order of the NA matrix is given by the number of nodes minus the number of pathological elements. Examples are discussed in order to illustrate the potential of the proposed pathological element-based active device models and the new formulation method in performing symbolic analysis of analog circuits. The improved formulation method is compared with traditional formulation methods, showing that the NA matrix is more compact and the generation of nonzero coefficients is reduced. As a consequence, the proposed formulation method is the most efficient one reported so far, since the CPU time and memory consumption is reduced when recursive determinant-expansion techniques are used to solve the NA matrix.

In analog integrated circuit design, iterations between electrical and physical syntheses to counterbalance layout-induced performance degradations should be avoided as much as possible. One possible solution involves the integration of traditionally separated electrical and physical synthesis phases by including layout-induced effects right into the electrical synthesis phase in what has been called parasitic-aware synthesis. This solution, as such, is not yet complete since there are geometric requirements (minimization of area or fulfillment of certain layout aspect ratio, among others) whose effects on the resulting parasitics are not usually considered during the electrical synthesis. In this paper, a layout-aware solution for analog cells that tackles both geometric and parasitic-aware electrical synthesis is proposed. Several design examples are provided.

Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.