Jae W. Hahn

Experimental analysis of pattern line width in digital maskless lithography

We study the distributions of line/space (L/S) patterns based on exposure dose variation using point array techniques (a type of digital maskless lithography). The intensity distributions of L/S patterns were simulated using the point array technique, and the pattern profiles were obtained by applying the effect of the photoresist contrast to the intensity distribution. As the dose increased, line width also increased. An experiment was performed to verify the simulation results. The minimum line widths of the L/S patterns were about 3.44 and 3.89 μm at laser power levels of 100% and 60%, respectively. The standard deviations of the line widths were 0.28 and 0.03 μm at the 4 and 13 μm L/S patterns, respectively.

Spatially resolvable optical emission spectrometer for analyzing density uniformity of semiconductor process plasma

We proposed a spatially resolved optical emission spectrometer (SROES) for analyzing the uniformity of plasma density for semiconductor processes. To enhance the spatial resolution of the SROES, we constructed a SROES system using a series of lenses, apertures, and pinholes. We calculated the spatial resolution of the SROES for the variation of pinhole size, and our calculated results were in good agreement with the measured spatial variation of the constructed SROES. The performance of the SROES was also verified by detecting the correlation between the distribution of a fluorine radical in inductively coupled plasma etch process and the etch rate of a SiO(2) film on a silicon wafer.

Dose performance characterization of extreme ultraviolet exposure system using enhanced energy sensitivity by resist contrast method

Characterization of the systematic and random dose errors of an extreme ultraviolet (EUV) exposure system is performed using an EUV resist as an energy sensor for fast and repeatable measurements. Dose error measurement is enabled by a critical phenomenon that occurs when the photoresist is exposed to a dose in the region between the onset dose of E1 and the clearing dose of E0 on the photoresist contrast curve, which results in enhanced sensitivity to the applied dose relative to the resist thickness. At doses near the enhanced sensitivity point, changes in the thickness of the photoresist can be detected based on the change in the reflected light intensity, and any intensity variations in a captured image of an exposed wafer can be reverse translated into the dose error of the exposure system. With a dose sensitivity that is capable of resolving approximately 0.25% of the nominal dose, it is possible to decompose the measured systematic in-band EUV dose error of the exposure system into the intrafield s...

Theoretical analysis of water droplet drying on different substrates on liquid crystal display panels heated with infrared radiation

ABSTRACT To analyze the drying process of water droplets on glass panels in liquid crystal displays (LCDs), we calculated the evaporation time of water droplets under infrared radiation heating. Assuming that the water droplets mainly sit on either a glass substrate or copper circuit line in LCD panels, we theoretically analyzed the heat transfer mechanisms during the evaporation process of the water droplets on the different substrates by including substrate reflection. From the calculations, we found that heat conduction between the water droplets and the copper substrate plays an important role in the beginning of the evaporation, resulting in longer evaporation times due to heat loss. To confirm the mechanism, we calculated the drying time of 150 µm droplets in the substrates, comparing the conduction and radiation energy. Finally, we calculated the process parameters of water droplet drying in the LCD industry.

Pixel-based learning method for an optimized photomask in optical lithography

Abstract. Circuit design is driven to the physical limit, and thus patterns on a wafer suffer from serious distortion due to the optical proximity effect. Advanced computational methods have been recommended for photomask optimization to solve this problem. However, this entails extremely high computational costs leading to problems including lengthy run time and complex set-up processes. This study proposes a pixel-based learning method for an optimized photomask that can be used as an optimized mask predictor. Optimized masks are prepared by a commercial tool, and the feature vectors and target label values are extracted. Feature vectors are composed of partial signals that are also used in simulation and observed at the center of the pixels. The target label values are determined by the existence of mask polygons at the pixel locations. A single-hidden-layer artificial neural network (ANN) is trained to learn the optimized masks. A stochastic gradient method is adopted for training to handle about 2 million samples. The masks that are predicted by an ANN show averaged edge placement error of 1.3 nm, exceeding that of an optimized mask by 1.0 nm, and averaged process variation band of 4.8 nm, which is lower than that of the optimized mask by 0.1 nm.

Prediction of biases for optical proximity correction through partial coherent identification

Abstract. Most approaches to model-based optical proximity correction (OPC) use an iterative algorithm to determine the optimum mask. Each iteration requires at least one simulation, which is the most time-consuming part of model-based OPC. As the layout becomes more complicated and the process conditions are driven to the physical limit, the required number of iterations increases dramatically. To overcome this problem, we propose a method to predict the OPC bias of layout segments with a single-hidden-layer neural network. The segments are characterized by length and based on intensities at the corresponding control points, and these features are used as input to the network, which is trained with an extreme learning machine. We obtain a best-error root mean square of 1.29 nm from training and test experiments for layout clips sampled from a random contact layer of a logic device. In addition, we reduced the iterations by 27.0% by initializing the biases in the trained network before performing the main iterations of the OPC algorithm.