| Summary: | 碩士 === 中原大學 === 機械工程研究所 === 91 === Photolithography is the key process of IC manufacturing and directly influences the limit of critical dimension (CD). The alignment and exposure represents two major technologies in modern photolithography. During the exposure stage the circuit patterns between two conjunctive layers may cause displacement because of the influence of the stepper, wafer condition and external environment. This leads to overlay error. If the overlay error exceeds the limit of fault tolerance, the out outcome of short circuit will decrease the yield of products. With the increase of the wafer diameter and the shrinking of the feature size, the control of the overlay error becomes the key factor of maintaining the yield of products. Therefore, the overlay error should be compensated by a more reliable method. The neural network has the ability to approximate any unknown nonlinear function, which is to take advantage to filter the overlay error caused by the wafer or environment, and to estimate the parameters of overlay error model with the least-square method. Can be then avoided any wrong adjustments of the stepper due to the wafer distortion.
Because the Radial Basis Function network has the property of rapid learning, it can be used as function-approximation to increase the efficiency of estimation. From the results, it is found that the hybrid neural network has the ability to filter out the random error of wafer and accurate parameters of overlay error model can be obtained.
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