informs ® doi 10.1287/opre.1050.0254 © 2005 INFORMS This paper concerns a method for digital circuit optimization based on formulating the problem as a geometric program (GP) or generalized geometric program (GGP), which can be transformed to a convex optimization problem and then very efficiently solved. We start with a basic gate scaling problem, with delay modeled as a simple resistor-capacitor (RC) time constant, and then add various layers of complexity and modeling accuracy, such as accounting for differing signal fall and rise times, and the effects of signal transition times. We then consider more complex formulations such as robust design over corners, multimode design, statistical design, and problems in which threshold and power supply voltage are also variables to be chosen. Finally, we look at the detailed design of gates and interconnect wires, again using a formulation that is compatible with GP or GGP.

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Abstract—We first consider the problem of determining the doping profile that minimizes base transit time in a (homojunction) bipolar junction transistor. We show that this problem can be formulated as a geometric program, a special type of optimization problem that can be transformed to a convex optimization problem, and therefore solved (globally) very efficiently. We then consider several extensions to the basic problem, such as accounting for velocity saturation, and adding constraints on doping gradient, current gain, base resistance, and breakdown voltage. We show that a similar approach can be used to maximize the cutoff frequency, taking into account junction capacitances and forward transit time. Finally, we show that the method extends to the case of heterojunction bipolar junction transistors, in which the doping profile, as well as the profile of the secondary semiconductor, are to be jointly optimized. Index Terms—Base doping profile, base transit time minimization, cutoff frequency maximization, geometric programming, Ge-profile optimization, optimal doping profile. I.

Abstract. We present a framework for designing and analyzing primal-dual interior-point methods for convex optimization. We assume that a self-concordant barrier for the convex domain of interest and the Legendre transformation of the barrier are both available to us. We directly apply the theory and techniques of interior-point methods to the given good formulation of the problem (as is, without a conic reformulation) using the very usual primal central path concept and a less usual version of a dual path concept. We show that many of the advantages of the primal-dual interior-point techniques are available to us in this framework and therefore, they are not intrinsically tied to the conic reformulation and the logarithmic homogeneity of the underlying barrier function.

In this paper we give a brief overview of a heuristic method for approximately solving a statistical digital circuit sizing problem, by reducing it to a related deterministic sizing problem that includes extra margins in each of the gate delays to account for the variation. Since the method is based on solving a deterministic sizing problem, it readily handles large-scale problems. Numerical experiments show that the resulting designs are often substantially better than one in which the variation in delay is ignored, and often quite close to the global optimum. Moreover, the designs seem to be good despite the simplicity of the statistical model (which ignores gate distribution shape, correlations, and so on). We illustrate the method on a 32-bit Ladner-Fischer adder, with a simple resistor-capacitor (RC) delay model, and a Pelgrom model of delay variation.

We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indexes 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. Index Terms---Amplifiers, analog circuits, circuit optimization, design automation, geometric programming, sensitivity. I.