KEYWORDS: Electric fields, Selenium, Monte Carlo methods, Tunable filters, Statistical analysis, Scanning electron microscopy, Inspection, Sensors, Semiconductors, Scattering
Scanning electron microscopes for semiconductor device inspection require high-throughput performance for full-wafer inspections. To achieve high-throughput inspections, we need to inspect a large field of view (FOV) with a high current. However, these inspection conditions cause the sample to experience electrical charging. This sample charging causes abnormal contrast and image distortions. In this paper, we show that the sample charging can be controlled by the irradiation current, and image distortion can also be minimized. We also show that the sample charging can be controlled by the voltage applied to the sample, and we explain the mechanism of charge formation.
In scanning electron microscope (SEM) image simulation, it is necessary to consider the charging of electron beam irradiation, which can be computationally intensive. Therefore, we have developed a neural network-based algorithm to generate SEM images by inputting the shape and material properties of semiconductor devices, after which various preprocesses are applied to the physical parameters to improve the accuracy. The contrast and visibility of the generated images are then compared with simulation results that are not included in the training dataset. As a preliminary result, we found that the physical parameters that affect charging, such as the relative permittivity and electron mobility of the material, can be predicted. The effect of acceptance is also considered in the training process to reproduce the changes in image quality depending on the type and arrangement of detectors.
We have been developing a charging simulator “CHARMs”, which is based on 3D finite element method, to predict the characteristics of signal from charged pattern on surface of semi-conductor during the SEM observation. We have constructed a new flexible model to simulate the in-solid electron scattering by the Langevin equation for “CHARMs”. Our new model can manipulate the size of the diffusion cloud of scattering electron by the parameters of it and calculate ten times faster than the conventional Monte Carlo (MC) method. In addition to that, the model has the possibility to accurately describe the scattering of low energy electron. We also confirmed that the same results of the conventional MC method can be obtained from the simulation model of metal pattern buried in the sample.
Monte Carlo-based SEM image simulation can reproduce SEM micrographs by calculating scattering events of primary electrons inside the target materials. By using the simulated SEM images, it is possible to optimize imaging conditions prior to the specimen observation, which could save time for finding suitable observation condition. However, a recent trend of miniaturized and 3-dimentional structures of semiconductor devices, and introduction of various novel materials have created a challenge for such SEM image simulation techniques; that is, more precise and accurate modeling is required. In this paper, we present a quantitatively accurate BSE simulation and a precise parameters setting in voltage contrast simulation, for both to reproduce experimental SEM images accurately. We apply these simulation techniques to optimize the accelerating voltage of SEM for sub-surface imaging, and to analyze a charge distribution on the insulating specimen under the electron irradiation. These applications promise the advancement in developing a new device by preparing inspecting condition in a timely manner.
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