Abstract
The sensitivity of simulated scanning electron microscopy (SEM) images to the various physical model ingredients is studied using an accurate, but slow simulator, to identify the most important ingredients to include in a reliable and fast SEM image simulator. The quantum mechanical transmission probability (QT) model and the electron-acoustical phonon scattering model are found to have the most significant effect on simulated 2D and 3D metrology results. The linewidth measurement error caused by not including these models in the simulation is less than 2 nm. Specifically, it was found from a comparison to experimental data that the QT model is essential in accurately predicting particular signal features in linescans such as “shadowing”. The simulator is compared with two other publicly available simulators, JMONSEL and CASINO, where the first one is also based on first-principle physics models and the latter one is using phenomenological models. CASINO is the fastest simulator on CPU, but Nebula on GPU is two orders of magnitude faster compared to a single threaded CPU simulation. Only up to 6% speed increase has been achieved by different model choices.
Highlights
As semiconductor devices shrink in size, it becomes crucial to inspect with optimized system parameters to minimize measurement errors [1,2]
The linewidth measurement error caused by not including these models in the simulation is less than 2 nm
We studied the sensitivity of simulated scanning electron microscopy (SEM) images to various firstprinciple physics models
Summary
As semiconductor devices shrink in size, it becomes crucial to inspect with optimized system parameters to minimize measurement errors [1,2]. For high-end transistors accurate edge positioning [3,4,5] becomes essential when structure sizes approach a few nanome ters. The dramatically changing height/width (aspect) ratio of lines [6] requires full 3D metrology [7,8,9]. Synthetic scanning elec tron microscope (SEM) images produced by Monte Carlo simulations can be a great aid in analyzing such complex metrology tasks [10,11,12]. The resulting images will depend on the physical models included in the simulator. It is essential to know what their influence is on the results
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