Simulation flow and model verification for laser direct-write lithography

Patterns written by laser direct write have a critical dimension (CD) bias dependence on the dose similar to other direct write methods, such as electron beam lithography, which can be explained by the exposure intensity distribution (EID) of the laser beam. In this study, a comprehensive model based on the EID is proposed to understand the pattern CD bias dependence on the dose, which is known as the exposure latitude. This model was supported by the results of the exposure tests on MicroChem S1800 resist on an Si wafer using the Heidelberg DWL66+ laser writer. The exposure latitudes of the patterns at both micrometer and submicrometer scale were measured. At the micrometer scale, the exposure latitudes were found to have no measurable dependence on the pattern size and the local pattern density. This conclusion does not hold at the submicrometer scale where the length scale is comparable to the width of the laser beam. This study proposes a way to convert the experimental exposure latitude data into the EID functions and fit these functions by assuming a Gaussian laser beam intensity distribution. All empirical observations are found to agree with the predictions made by the EID model. The authors also show that this model can help achieve dimensional accuracy, especially when there is a change in the exposure process. Moreover, it can be used to analyze and minimize the CD inconsistency between different runs found in the experiments. To reduce such inconsistency, overexposing combined with a negative bias applied to the pattern is suggested.

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U-Net convolutional neural network-based modification method for precise fabrication of three-dimensional microstructures using laser direct writing lithography

Sun

Yin

Jiang

et al. 2021

Opt. Express

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In this paper, a modification method based on a U-Net convolutional neural network is proposed for the precise fabrication of three-dimensional microstructures using laser direct writing lithography (LDWL). In order to build the correspondence between the exposure intensity distribution data imported to the laser direct writing system and the surface profile data of the actual fabricated microstructure, these two kinds of data are used as training tensors of the U-Net convolutional neural network, which is proved to be capable of generating their accurate mapping relations. By employing such mapping relations to modify the initial designed exposure intensity data of the parabolic and saddle concave micro-lens with an aperture of 24µm×24µm, it is demonstrated that their fabrication precision, characterized by the mean squared error (MSE) and the peak signal-to-noise ratio (PSNR) between the fabricated and the designed microstructure, can be improved significantly. Specifically, the MSE of the parabolic and saddle concave micro-lens decreased from 100 to 17 and 151 to 50, respectively, and the PSNR increased from 22dB to 29dB and 20dB to 25dB, respectively. Furthermore, the effect of laser beam shaping using these two kinds of micro-lens has also been improved considerably. This study provides a new solution for the fabrication of high-precision three-dimensional microstructures by LDWL.

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Simulation flow and model verification for laser direct-write lithography

Cited by 3 publications

References 34 publications

Photoactive resin formulations and composites for optical 3D and 4D printing of functional materials and devices

Photoactive resin formulations and composites for optical 3D and 4D printing of functional materials and devices

Comprehensive modeling of the lithographic errors in laser direct write

U-Net convolutional neural network-based modification method for precise fabrication of three-dimensional microstructures using laser direct writing lithography

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