The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 07, 1999
Filed:
Apr. 07, 1998
Andrew Roger Conn, Mount Vernon, NY (US);
Rudolf Adriaan Haring, Cortlandt Manor, NY (US);
Chandramouli Visweswariah, Croton-on-Hudson, NY (US);
International Business Machines Corporation, Armonk, NY (US);
Abstract
A method of incorporating noise considerations during circuit optimization includes the steps of: specifying a circuit schematic to be optimized; specifying at least one noise criterion as a noise measurement, including the signal to be checked for noise, the sub-interval of time of interest, and the maximum allowable noise deviation; providing each noise criterion as either a semi-infinite constraint or a semi-infinite objective function; specifying at least one variable of the optimization; converting the semi-infinite noise constraints and the semi-infinite noise objective functions into time-integral equality constraints; optionally, if required, providing additional optimization criteria other than noise as, for each such criterion, either objective functions or constraints; creating a merit function to be minimized to solve the optimization problem; simulating the circuit in the time-domain; computing the values of the objective functions and constraints; efficiently computing the gradients of the merit function of the optimizer (including contributions of all objective functions and constraints and the time-integrals representing noise considerations) preferably by means of a single adjoint analysis; iteratively providing the constraint values, the objective function values and the gradients of the merit function to a nonlinear optimizer; and continuing the optimization iterations to convergence.