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In this paper 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. 1.

We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indices for the overall amplifier, such as bandwidth, gain, rise-time, delay, output signal swing, linearity, and noise performance, in a complicated and nonlinear fashion, making optimization of the feedback gains a challenging problem. In this paper we show that this problem, though complicated and nonlinear, can be formulated as a special type of optimization problem called geometric programming. Geometric programs can be solved globally and efficiently using recently developed interior-point methods. Our method therefore gives a complete solution to the problem of optimally allocating local feedback gains, taking into account a wide variety of constraints. 1 1

This paper presents an new direct--fitting method to generate posynomial response surface models with arbitrary constant exponents for linear and nonlinear performance parameters of analog integrated circuits. Posynomial models enable the use of efficient geometric programming techniques for circuit sizing and optimization. The automatic generation avoids the time--consuming nature and inaccuracies of handcrafted analytic model generation. The technique is based on the fitting of posynomial model templates to numerical data from SPICE simulations. Attention is paid to estimating the relative `goodness--of--fit' of the generated models. Experimental results illustrate the significantly better accuracy of the new approach.

Analog performance space exploration identifies the range of feasible performance values of a given circuit topology. It is an extremely challenging task of great importance to topology selection and hierarchical sizing. In this paper, a novel technique for the efficient simulation-based exploration of high-dimensional performance spaces is presented. To this end, fundamental circuit design knowledge is described by constraint functions. Based on a linearization of the latter and of the circuit performance functions, a description of the feasible performance range in the form of a polytope is derived. Moreover, the approach is integrated into a hierarchical sizing method, where it propagates topological and technological constraints bottom-up. Practical application results demonstrate the efficiency and usefulness of the new method. 1.

We present a novel technique to automatically calculate an initial sizing of analog circuits that conforms to good design practice. The method is purely (DC) simulation-based and does not need symbolic design equations or user design knowledge. It identifies the space of feasible design parameters based on implicit specifications, which arise from the circuit topology. A sizing centered within this space is obtained by iteratively solving a maximum volume ellipsoid problem on approximations to the feasible parameter space. The result is well-suited as initial sizing because it safely satisfies all implicit specifications. Experimental results demonstrate the efficiency and reliability of our method. 1.

We present an efficient and accurate gate sizing tool that employs a novel piecewise convex delay model, handling both rise and fall delays, for static CMOS gates. The delay model is used in a new version of a gate-sizing tool called Forge, which not only exhibits optimality, but also efficiently produces the area versus delay tradeoff curve for a block in one step. Forge includes a realistic delay propagation scheme that combines arrival times and slew-rates. Forge is 6.4X faster than a commercial transistor sizing tool, while achieving better delay targets and uses 28 % less transistor area for specific delay targets, on average.

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 a#ordable; 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 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 applicationspecific 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.

In this paper we propose a novel analog design optimization methodology to address two key aspects of top-down system-level design: (1) how to optimally compare and select analog system architectures in the early phases of design; and (2) how to hierarchically propagate performance specifications from system level to circuit level to enable independent circuit block design. Importantly, due to the inaccuracy of early-stage system-level models, and the increasing magnitude of process and environmental variations, the system-level exploration must leave sufficient design margin to ensure a successful late-stage implementation. Therefore, instead of minimizing a design objective function, and thereby converging on a constraint boundary, we apply a novel performance centering optimization. Our proposed methodology centers the analog design in the performance space, and maximizes the distance to all constraint boundaries. We demonstrate that this early-stage design margin, which is measured by the volume of the inscribed ellipsoid lying inside the performance constraints, provides an excellent quality measure for comparing different system architectures. The efficacy of our performance centering approach is shown for analog design examples, including a complete clock data recovery system design and implementation. 1.

We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and non-convex constraints. Adding a limited set of non-convex constraints can improve the accuracy of convex equationbased optimization, without compromising global optimality. These constraints allow increased accuracy for critical modeling equations, such as the relationship between gm and IDS. To demonstrate the design methodology, a foldedcascode amplifier is designed in a 0.18 µm technology for varying speed requirements and is compared with simulations and designs obtained from geometric programming. Categories and Subject Descriptors: