Abstract:The ever-shorter time-to-market calls for efficient robust IC design algorithms. Robust circuits satisfy all design requirements across a range of operating conditions and manufacturing process variations. In the paper we propose an automated robust IC design and optimization process derived from the design algorithms utilized manually by experienced analog IC designers. We achieve this by transforming the robust design and optimization problem into a constrained optimization problem using tradeoff planes and penalty functions. We illustrate the method on a robust differential amplifier design problem. Circuits resulting from several different optimization runs show that a computer can not only improve existing circuit designs but it can also size a circuit with very little initial knowledge. The resulting circuits have comparable or even superior performance to humanly designed circuits. The method could easily take advantage of parallel processing but is still efficient enough to be run on a single computer.
SUMMARYAn efficient algorithm is given for finding all stable equilibrium states of a cellular neural network. The method is based upon a new theorem concerning the network dynamics. The theorem implies the implementation of a simple sign test which, combined with a binary tree search, efficiently performs the task of searching the equilibria. Since the proposed algorithm uses information about the network architecture and dynamic behaviour, it exhibits two important advantages over other more general methods for searching the equilibria of resistive piecewise-linear circuits. First, the regions which cannot possibly include stable steady states can be eliminated even before starting to perform the algorithm. Secondly, there is no need to solve any set of linear equations, which is usually the most tedious part of equilibrium-searching methods.
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