We describe a new method for determining component values and transistor dimensions for CMOS operational amplifiers (op-amps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result, the amplifier design problem can be expressed as a special form of optimization problem called geometric programming, for which very efficient global optimization methods have been developed. As a consequence we can efficiently determine globally optimal amplifier designs or globally optimal tradeoffs among competing performance measures such as power, open-loop gain, and bandwidth. Our method, therefore, yields completely automated sizing of (globally) optimal CMOS amplifiers, directly from specifications. In this paper, we apply this method to a specific widely used operational amplifier architecture, showing in detail how to formulate the design problem as a geometric program. We compute globally optimal tradeoff curves relating performance measures such as power dissipation, unity-gain bandwidth, and open-loop gain. We show how the method can he used to size robust designs, i.e., designs guaranteed to meet the specifications for a variety of process conditions and parameters.

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Optimal design of a CMOS op-amp via geometric programming

We survey contemporary optimization techniques and relate these to optimization problems which arise in the design of integrated circuits. Theory, algorithms and programs are reviewed, and an assessment is made of the impact optimization has had and will have on integrated-circuit design. Integrated circuits are characterized by complex tradeoffs between multiple nonlinear objectives with multiple nonlinear and sometimes nonconvex constraints. Function and gradient evaluations require the solution of very large sets of nonlinear differential equations, consequently they are inaccurate and extremely expensive. Furthermore, the partmeters to be optimized are subject to inherent statistical fluctuations. We focus on those multiobjective constrained optimization techniques which are appropriate to this environment.

A survey of optimization techniques for integrated-circuit design

A constrained minimax problem is converted to minimization of a sequence of unconstrained and continuously differentiable functions in a manner similar to Morrison's method for constrained optimization. One can thus apply any efficient gradient minimization technique to do the unconstrained minimization at each step of the sequence. Based on this approach, two algorithms are proposed, where the first one is simpler to program, and the second one is faster in general. To show the efficiency of the algorithms even for unconstrained problems, examples are taken to compare the two algorithms with recent methods in the literature. It is found that the second algorithm converges faster with respect to the other methods. Several constrained examples are also tried and the results are presented.

New algorithms for constrained minimax optimization

Antenna patterns can be synthesized using a new nonlinear minimax optimization method with sure convergence properties. Not requiring derivatives, the proposed method is general and easy to use so that it might be applied to a wide variety of nonlinear synthesis problems for which analytical solutions are not known. To test the algorithm a group of test problems for which exact analytical solutions are known has been considered, namely, optimization of Dolph-Chebyshev arrays by spacing variation. The method is further applied to find the element positions in nonuniformly spaced linear arrays with uniform excitation that produce minimized (equal) sidelobe levels, and comparisons are made with conventional Dolph-Chebyshev arrays.

/pdf/synthesis-of-nonuniformly-spaced-arrays-using-a-general-1z0pf0bzh1.pdf

Synthesis of nonuniformly spaced arrays using a general nonlinear minimax optimisation method

Communication satellites have evolved from a radio relay in space to encompass other applications such as processing and meteorology. The demand for high capacity communication satellites has led in the past two decades to the development of multibeam antennas, a landmark in satellite antenna technology. Satellite antennas are classified into three categories according to function: communication; tracking, telemetry, and command (TT and C); and special satellite antennas. While the application of shaped beam antennas has been developed to its fullest extent as in INTELSAT VI and VII, an active monolithic microwave integrated circuit (MMIC) phased array will play an important role in future satellite antenna technology. Examples of each state-of-the-art satellite antenna technology and the process of space qualification are presented briefly. >

Satellite antennas

A new minimax network optimization algorithm not requiring derivatives has been developed. It is based on successive linear approximations to the nonlinear functions defining the problem. Adequate modeling of distributed parameter circuits for optimization purposes often involves parasitic, etc., which makes the gradient computation by the adjoint network method or related methods rather complicated, and often numerical errors are introduced in the gradients. Consequently, the algorithm is found to be of particular relevance in optimum design of practical microwave networks. The relative advantages of the proposed algorithm are established by comparison with known gradient and nongradient algorithms based on optimization of cascaded transmission-line transformers. The relevance to microwave filter design is demonstrated by an example which represents an improvement of analytical filter design results. Finally, optimum broad-band design of a practical coaxial transferred-electron reflection-type amplilier is carried out by means of the proposed method. The results are supported by experimental verification.

/pdf/efficient-minimax-design-of-networks-without-using-21bk51705l.pdf

Efficient Minimax Design of Networks without Using Derivatives

In the above paper, on page 804, the logic of the flow diagram in Fig. 1 appears to be somewhat in error. The corrected flow diagram is shown here in Fig 1.

O. Nielsen

Papers

Efficient Minimax Design of Networks without Using Derivatives

Correction to "Efficient Minimax Design of Networks Without Using Derivatives"