Jun-Taek Park

Resist pattern collapse with top rounding resist profile

The pattern size is reduced as the device is more integrated. The resist deformation phenomenon has been a serious problem for under 100 nm line width patterns. In this study, a simulation tool for pattern collapse is created by using the existing beam sway model, and the effects of resist profile that exert on the pattern collapse have been studied. The simulated results show that the pattern collapse phenomenon is reduced for the top rounding resist profile rather than for vertical profiles.

Numerical modeling of microsegregation in binary alloys

In this study, an advanced two-dimensional model for prediction of microsegregation during solidification is suggested on the basis of a previous one-dimensional model. Especially, for the growth of a secondary arm, a simple and effective mathematical function was established to show the growing pattern. The solute diffusion in the solid phase was calculated by a finite-difference method (FDM). The solid-liquid interface movement was considered to be in local equilibrium state indicated by the phase diagram. Experiments for unidirectional solidification of Al-4.9 wt% Cu binary alloy were carried out in order to examine the reasonability and feasibility of this model. The concentration profile of solute and eutectic fraction predicted by the simulation agreed well with those found from experiment. According to the results, it is believed that the pattern function introduced in this study is reasonable for prediction of the microsegregation in dendritic growth.

Process Proximity Correction by Neural Networks

Making an accurate and quick critical dimension (CD) prediction is required for higher integrated device. Because simulation tools are consisted of many process parameters and models, it is hard that process parameters are optimized to match with the CD results for various patterns. This paper presents a method of improving accuracy of predicting CD results by applying the CD difference between simulation and experimental data value to neural network algorithm to reduce the CD difference caused by optical proximity effect.

CD prediction by threshold energy resist model (TERM)

In the step of developing lithography devices, VTRM (Variable Threshold Resist Model), aerial image based simulation, is useful to get feedback for a resist process margin. VTRM is also used to compensate for the mask patterns OPE (Optical Proximity Effect) and to optimize the optical system rather than the full simulation method that requires all the process parameters. However, VTRM has shown some problems that the exposure dose and focus should be fixed in one special condition to improve the prediction accuracy and cannot be combined together in one equation for patterns size and type variation. In this paper, a new simulation method that has more accuracy and wider applicability than the VTRM method was suggested. The new simulation method can represent the photolithography process with simple formula. The parameters of this formula are composed of exposure dose and defocus as input components, CD as output component, and all the resist processes are kept constant to keep consistency for other resist processes. The first technical improvement of this equation is to use process-matched aerial image derived from the fact that the aerial images at the top resist surface cannot represent the bulk resist energy distribution. The second one is to introduce a new concept TERM (Threshold Energy Resist Model). The energy threshold level is used instead of the aerial images intensity threshold level in order to predict CDs. Energy threshold level can be simply found by the simple equation and an experiment. The simple equation consists of a mask edge opening energy, the mask edge image intensity, and a process factor.

Simulation of complex resist pattern collapse with mechanical modeling

High aspect ration resist patterns with dimensions below 100 nm often bend, break or tear. These phenomena are generically called “resist pattern collapse”. Pattern collapse is a very serious problem in fine patterning of less than 100 nm critical dimension (CD), so that it decreases the yield. In order to mechanically analyze this phenomenon and create its simulator, two models have been made and compared. In this paper, various approaches with various analyses are made to understand pattern collapse. Also, the critical aspect ratio for 100 nm node, that determines whether pattern collapse happens or not, can be calculated with these approaches. Finally tear type caused by insufficiency of adhesion strength between the substrate and the resist is analyzed with a point of view of the surface free energy.

Threshold energy resist model for CD prediction

The threshold resist model based on only aerial image is less time consuming and is sometimes more efficient than the full simulation model based on mathematically analyzing the whole complicated process of photolithography. But this model still contains a disadvantage that the prediction is limited in various situations. In this paper, the new threshold resist model to predict the Critical Dimension (CD) on the wafer is presented. This model has a functional form consisted of the aerial image intensity and its slope. More than 90% of prediction accuracy in various patterns with respect to pattern sizes is obtained by using the new model for 248 nm.

Effect of EUV light scattering from the rough absorber and buffer side wall

Summary form only given. In this paper we study the roughness of the multi-reflective surface layers by Monte-Carlo Methods and described by PSD (power spectral density) function. Surface roughness and side wall roughness are investigated and compared.

Prediction of high NA ArF lithography capabilities for 70 nm technology node using simulation

Summary form only given. In this article, we will present the possible performance of high numerical aperture (NA) 193 nm lithography for 70 nm technology node using in-house simulator. This simulator can calculate the vector diffraction phenomenon which may not be neglected for high NA imaging. At low k1 optical lithography, certain illumination and mask techniques are needed to overcome process difficulties. These include off-axis illumination (OAI), alternating phase-shifting masks (alt PSMs) and chromeless phase lithography (CPL). The solution for successful low k1 imaging without any improvement of tools and materials should be the use of good combinations of several existing techniques. In addition, how the optical effects with low k1 imaging is changing should be carefully considered. This presentation will discuss the various simulation works for imaging techniques to support 70 nm node with high NA ArF lithography. Especially, we will investigate the results of critical dimension variations, exposure dose latitudes, depth of focus, MEEF and NILS with varying illumination conditions and mask types.

Threshold energy resist model for critical dimension prediction

The threshold energy resist model based on the aerial image is less time consuming and sometimes more efficient than the full simulation model based on mathematical analysis of the whole complicated photolithography process. Moreover, this model still contains a disadvantage that its prediction is limited in various situations. In this paper, we report the new threshold resist model to predict the critical dimension (CD) on the wafer is presented. This model has a functional form that is consisted of the aerial image intensity and its slope. The contours of a resist pattern are determined from the aerial image contours of the process matched by using a functional form. High prediction accuracy in various patterns with respect to pattern sizes is obtained by using the new model.

Extraction of exposure parameters by using neural networks

Dill’s ABC parameters are key parameters for the simulation of photolithography patterning. The exposure parameters of each resist should be exactly known to simulate the desired pattern. In ordinary extracting methods of Dill’s ABC parameters, the changed refractive index and the absorption coefficient of photoresist are needed during exposure process. Generally, these methods are not easy to be applied in a normal fab because of a difficulty of in-situ measuring. An empirical E0 (dose-to-clear) swing curve is used to extract ABC exposure parameters previously by our group. Dill’s ABC parameters are not independent from each other and different values of them would cause the dose to clear swing curve variation. By using the known relationship of ABC parameters, the experimental swing curves are to be matched with the simulated ones in order to extract the parameters. But sometimes this method is not easy in matching the procedure and performing simulation. This procedure would take much time for matching between the experimental data and the simulation by the naked eyes, and also the simulations are performed over and over again for different conditions. In this paper, Dill’s ABC parameters were extracted by applying the values, which are quantitatively determined by measuring the mean value, period, slope, and amplitude of the swing curve, to the neural network algorithm. As a result, Dill’s ABC parameters were able to rapidly and accurately extracted with some of the quantified values of the swing curve. This method of extracting the exposure parameters can be used in a normal fab so that any engineer can easily obtain the exposure parameters and apply them to the simulation tools.