Determination and analysis of minimum dose for achieving vertical sidewall in electron-beam lithography

Zhao, Xunwang; Dai, Qing; Lee, Sang Yup; Choi, Jin; Lee, Sang‐Hee; Shin, In-Kyun; Jeon, Chan-Uk

doi:10.1116/1.4901013

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of developing effective and efficient schemes for correcting the proximity effect has been well recognized for a long time, and various methods were proposed and implemented by many researchers [1]- [14]. As the feature size decreases well below microns into nanoscale (100 nm or less), the line edge roughness (LER) can significantly affect the minimum feature size and the maximum circuit density realizable in practice.…”

Section: Introductionmentioning

confidence: 99%

A Novel Analytic Approach to Model Line Edge Roughness using Stochastic Exposure Distribution in Electron-beam Lithography

Guo,

Lee

2016

Preprint

Self Cite

View full text Add to dashboard Cite

The line edge roughness (LER) becomes a issue of e-beam lithography when feature size is reduced into nanometers. Therefore, minimizing the LER is a important method to increase the density of circuit patterns. One of the possible ways is through simulation. The stochastic exposure distributions in the resist is generated by the Monte Carlo simulation. In addition a resist development simulation needs to be carried out. Although there are several ways to simulate or estimate LER but none of them can reveal as much the inner relationship between LER and different parameters as theocratical analysing methods can do. In this paper, a new approach to analytically derive the LER based on the statistical exposure, is described. Our approach is based on analytic model of stochastic exposure distribution, deriving standard deviation of exposure and analyzing the variance of edge location after development. Even though it may not be a complete modeling of LER, it can still show some strong relationship between LER and some inner parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Analytic Approach to Model Line Edge Roughness using Stochastic Exposure Distribution in Electron-beam Lithography

Guo,

Lee

2016

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a previous work [8,9], a 3-D exposure model was introduced to analyze effects of beam energy, resist thickness, feature size, and developing time on the spatial exposure distribution, i.e., depth-dependent proximity effect. Subsequently, the issue of 3-D proximity effect correction was addressed, deriving the spatial dose distributions required for three different types of resist profiles using a simple search method, and only the simulation results were presented [10,11].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Spatial Dose Distribution for Controlling Sidewall Shape in Electron-beam Lithography

2015

Preprint

View full text Add to dashboard Cite

Electron-beam (e-beam) lithography is widely employed in fabrication of 2-D patterns and 3-D structures. A certain type or shape of the sidewall in the remaining resist profile may be desired in an application, e.g., an undercut for lift-off and a vertical sidewall for etching, or required for a device. Also, as the feature size is decreased well below a micron, a small variation of the sidewall slope can lead to a significant (relative) CD error in certain layers of resist. Therefore, it is important to understand effects of spatial dose distribution on sidewall shape and be able to achieve the desired shape. In this study, via simulation, the relationship among the total dose, spatial distribution of dose, developing time and sidewall shape, and performance of the method developed to optimize the dose distribution for a target sidewall shape have been analyzed. The simulation results have been verified through experiments.

show abstract

Generalized performance optimization for massively-parallel electron-beam systems

Lee

Hasan

2023

Journal of Vacuum Science &Amp; Technology B

View full text Add to dashboard Cite

The massively-parallel e-beam system (MPES) provides a large number of programmable beams of which the optimal use is critical to maximizing the efficiency of the system. In our previous study of proximity effect correction (PEC) under the constraints of the MPES, the critical dimension error and the line edge roughness were minimized by reducing the feature area to be exposed and varying the dose spatially. In another study, a method to reduce the exposing time while still ensuring a near-optimal PEC result was developed. In this method, the maximum dose difference between two regions of a feature was carefully decreased after first obtaining the optimal linewidth reduction and the spatial dose distribution for the PEC. However, this method designed with an emphasis on the PEC and simplicity may miss the optimal result due to the fixed order of performance metrics considered and was developed for a single feature. To address these limitations, an adaptive optimization method that can handle any combination of performance metrics in a cost function is proposed. It allows for more flexible optimization and can achieve better results than the old method. Additionally, the optimization method is extended for large-scale patterns with uniform features, such as line-space patterns. Also, an effective way to handle the recursive effect among critical locations in a large-scale pattern is described. The simulation results show that the proposed optimization method outperforms the old method in terms of the cost function and linewidth uniformity among the features.

show abstract

Determination and analysis of minimum dose for achieving vertical sidewall in electron-beam lithography

Cited by 3 publications

References 9 publications

A Novel Analytic Approach to Model Line Edge Roughness using Stochastic Exposure Distribution in Electron-beam Lithography

A Novel Analytic Approach to Model Line Edge Roughness using Stochastic Exposure Distribution in Electron-beam Lithography

Optimization of Spatial Dose Distribution for Controlling Sidewall Shape in Electron-beam Lithography

Generalized performance optimization for massively-parallel electron-beam systems

Contact Info

Product

Resources

About