Signamization is an automated process to optimize the NA and (sigma) settings of a stepper for a given combination of critical features and CD tolerances, basing on the E-D Forest methodology. It has been applied to a variety of feature combinations using disk and annular illuminations, binary intensity mask and phase-shifting mask. They are based on pre-simulated aerial images. This paper deals with signamization of resist images derived from aerial image data. The aerial image is converted to resist image analytically in real time using the lumped parameter model, which make any pre-simulated aerial images; such as those for binary intensity masks, attenuated phase shifting masks, alternating phase shifting masks, disk illumination. Nikon ring illumination, and Canon ring illumination, usable for any resist system characterized by resist thickness, absorption coefficient (alpha) , and contrast (gamma) , without having to simulate for each particular resist system. The signamization results show that in addition to the optimum NA and (sigma) , there is a (gamma) threshold above which further improvement is unrewarding, a higher (alpha) can improve imaging performance, and that the resist thickness plays an important role only when the reflection swing ratio is large or when it is reduced significantly. When this happens, together with (alpha) becoming very small and (gamma) very large, and resist image approaches that aerial image, showing the validity of the lumped parameter model for signamization and the computer programming.