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The W8502EP IC-CAP Simulation and Analysis enables analysis or simulation of device behavior using the provided Advanced Design System (ADS) or SPICE3 simulators or by linking directly to supported external simulators. Comparisons of simulated to measurement or target data can then be achieved.

The Analysis Module also includes 13 robust optimization algorithms and the Plot Optimizer. The Plot Optimizer enables quick optimizations to be set up directly from any IC-CAP plot.

The ADS Linear and Transient simulators are provided with the Analysis Module. The ADS Harmonic Balance simulator can purchased separately for use with IC-CAP.

The following simulators are included in the Simulation and Analysis:

Simulator Vendor Remote Link Supported?*
SPECTRE Cadence Yes
HSPICE Synopsys Yes
SABER Synopsys Yes
ELDO Mentor Yes

* IC-CAP supports all simulators locally, where IC-CAP and the simulator are running on the same machine. In some cases, IC-CAP and the simulator reside on separate machines and communicate via remote link. These remote links are only supported on Solaris and LINUX operating systems

The following optimizers are provided in the Analysis Module:

IC-CAP Optimization Algorithms
Algorithm Description
Levenberg-Marquardt Non-linear search method with least-squares error function.
Random Random search method with stochastic gradient error function.
Hybrid (Random/LM) Combination of Random and Levenberg-Marquardt algorithms and error functions.
Sensitivity Analysis Single-point or infinitesimal sensitivity analysis of a design variable. Prints partial derivatives with respect to each parameter.
Random (Gucker) Random search method with least-squares error function.
Gradient Gradient search method with least-squares error function.
Random Minimax Random search method with minimax error function.
Gradient Minimax Gradient search method with minimax error function.
Quasi-Newton Quasi-Newton search method with least-squares error function.
Least Pth Quasi-Newton search method with least Pth error function.
Minimax Two-stage, Gauss-Newton/Quasi-Newton method with minimax error function.
Hybrid (Random/Quasi-Newton) Combines the Random and Quasi-Newton search methods.
Genetic Direct search method using evolving parameter sets.