Bayesian optimization-based estimation of effective reaction radius of chemically amplified resist in acid catalyzed deprotection reaction

Abstract

Chemically amplified resist realized the high sensitivity in semiconductor manufacture by utilizing the acid catalyzed deprotection reaction of protected polymer. One of the parameters reflecting the reaction ability of resist is effective reaction radius of deprotection (Rp). However, Rp cannot be achieved by experimental measurements. Similarly, the concentration of protected units at dissolving point of developer (Cth) cannot be measured as well. Cth strongly related to the generation of defects of resist pattern after development. In the previous study, a simulation model of electron beam (EB) lithography processes from beam exposure to development was constructed. To calibrate the EB lithography model, these parameters must be estimated. Resist pattern taken by scanning electron microscopy (SEM) was used to compare with the simulation calculated resist pattern. The difference between experimental and simulation data was used to evaluate the influence of Rp and Cth on the simulation model. However, during this estimating process, the simulation model must compute every time when Rp or Cth changes. This is time-consuming and the computational cost is high. To reduce the iterative computation, Bayesian optimization (BO) based on Gaussian process regression (GPR) with Matérn covariance kernel was applied to accelerate the optimal pace. BO can optimize the parameter globally and locate the value that worthiest to test. By using BO, the iterative calculation was magnificently reduced from 140 to 35. The probable values of Rp or Cth were found. Furthermore, to prove the ability of BO, the result was verified by grid search method.