초록 ▼

A system for adaptively controlling a wide variety of complex processes, despite changes in process parameters and despite both sudden and systematic drifts in the process, uses response surfaces described by quadratic equations or polynomials of any order. The system estimates the dynamic component

A system for adaptively controlling a wide variety of complex processes, despite changes in process parameters and despite both sudden and systematic drifts in the process, uses response surfaces described by quadratic equations or polynomials of any order. The system estimates the dynamic component of a drifting process or system and thereby identifies the trend of output response variables of the controlled process. Using this information, the system predicts future outputs based on a history of past and present inputs and outputs, thereby recommending the necessary control action or recipe (set of input parameters) to cancel out the drifting trend. A specific embodiment is a system for the adaptive control of photoresist thickness, uniformity, and dispense volume in the spin coating of wafers in integrated circuit manufacturing. Methods used in the adaptive control system are adaptable to control many processes not readily modeled by physical equations.

대표청구항 ▼

[ What is claimed is:] [1.]1. A method for adaptively controlling a process operating on a sequence of samples according to a recipe having recipe values, said method comprising the steps of:(a) initializing an adaptive controller by setting initial parameter values and setting nominal recipe values

[ What is claimed is:] [1.]1. A method for adaptively controlling a process operating on a sequence of samples according to a recipe having recipe values, said method comprising the steps of:(a) initializing an adaptive controller by setting initial parameter values and setting nominal recipe values, said adaptive controller being described by a first parameter T.sub.r, a standard deviation of said first parameter s.sub.r, an extrapolated value T.sub.e3 of said first parameter, an extrapolated value of standard deviation s.sub.e3 of said first parameter, a model value T.sub.m2 (a) of said first parameter, a model value of standard deviation s.sub.m2 (b) of said first parameter, a mean value T(x).sub.p3 of said first parameter, a mean value of standard deviation s(x).sub.p3 of said first parameter, a desired mean value T.sub.d of said first parameter, and a desired mean value of standard deviation s.sub.d of said first parameter;(b) computing a model-predicted parameter value using first equation(c) processing a first sample of said sequence, measuring a first parameter of said first sample multiple times to obtain a mean value and sample standard deviation for said first sample;(d) computing the resulting error of said first parameter and updating the parameters of said first sample by using equations ject to aand ject to bT.sub.m (a,x)=a.sub.0 +a.sub.1 x.sub.1 +a.sub.2 x.sub.2 +a.sub.11 x.sub.1.sup.2 +a.sub.12 x.sub.12s.sub.m (b,x)=b.sub.0 +b.sub.1 x.sub.1 +b.sub.2 x.sub.2 +b.sub.11 x.sub.1.sup.2 +b.sub.12 x.sub.12(e) processing at least a second sample and extrapolating to find the value of the next sample point and using it, as if it is the true sample, to update adaptive controller parameters by using equations [T.sub.e3 -T.sub.m2 (a)].sup.2 Equation (10) ject to a;and ject to b,(f) computing the optimum recipe to use on the next run by using the simultaneous nonlinear equations(g) repeating the above steps (b) through (f) until a stopping condition is met.