Resist profile development modeling

The prediction of a given contour shape, i.e., resist profile, requires both exposure and development models. We will first examine the various exposure models which have been developed and then combine these with development studies in order to predict resist profiles and compare them with experiment. 

Microposit 2400 resist, manufactured by Shipley Co., has been shown to be sensitive at this short wavelength ( ) and is being employed with the deep UV stepper. We report here the results of resist profile modeling for submicron photolithography at 248 nm. Various model parameters needed as input data were measured to characterize exposure and development of the resist. 

A good measure of the resist profile is the sidewall angle, which can be predicted with the lumped parameter model. To derive an expression for the sidewall slope, it is customary to rewrite Eq. (12.135) in terms of development rate as... 

The most important application of SCM is two-dimensional dopant concentration profiling in semiconductors. However, the raw capacitance-voltage data obtained from SCM must be converted by a mathematical model into a dopant concentration. Therefore, development and validation of appropriate models represents a large part of SCM methodology. To validate the various models, other experimental techniques must be employed to measure and verify the dopant profiles independently. SCM data are usually compared to secondary ion mass spectrometry (SIMS) measurements of dopant concentrations. Spreading resistance profiling (SRP) and computer simulations are also employed to check model validity. 

Auger electron spectroscopy with depth profiling via argon ion etching (position and thickness of near-surface layer), transmission electron microscopy of ultramicrotomed cross-sections (physical internal structure), elemental analysis (extent of metal salt conversion), and surface electrical resistivity versus temperature profiles (continuity of near-surface layer). The data from these techniques were used cooperatively to develop a model for these microcomposite polyimide films. The model represents the sample as three distinct regions. Fig. 1. The bulk of the film contains either converted (e.g. Ag) or nonconverted (e.g. C0CI2) additive in a predominately polyimide environment. An oxide-rich (e.g. 0 ) or metal-rich layer (e.g. Ag, Au) interspersed with polyimide accounts for the second region. 

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