Yee Mei Foong

Optimizing OPC data sampling based on "orthogonal vector space"

With shrinking feature sizes and error budgets in OPC models, effective pattern coverage and accurate measurement become more and more challenging. The goal of pattern selection is to maximize the efficiency of gauges used in model calibration. By optimizing sample plan for model calibration, we can reduce the metrology requirement and modeling turn-around time, without sacrificing the model accuracy and stability. With the Tachyon pattern-selection-tool, we seek to parameterize the patterns, by assessing dominant characteristics of the surroundings of the point of interest. This allows us to represent each pattern with one vector in a finite-dimensional space, and the entire patterns pool with a set of vectors. A reduced but representative set of patterns can then be automatically selected from the original full set sample data, based on certain coverage criteria. In this paper, we prove that the model built with 56% reduced wafer data could achieve comparable quality as the model built with full set data.

Process window enhancement using advanced RET techniques for 20nm contact layer

At the 20nm technology node, it is challenging for simple resolution enhancements techniques (RET) to achieve sufficient process margin due to significant coupling effects for dense features. Advanced computational lithography techniques including Source Mask Optimization (SMO), thick mask modeling (M3D), Model Based Sub Resolution Assist Features (MB-SRAF) and Process Window Solver (PW Solver) methods are now required in the mask correction processes to achieve optimal lithographic goals. An OPC solution must not only converge to a nominal condition with high fidelity, but also provide this fidelity over an acceptable process window condition. The solution must also be sufficiently robust to account for potential scanner or OPC model tuning. In many cases, it is observed that with even a small change in OPC parameters, the mask correction could have a big change, therefore making OPC optimization quite challenging. On top of this, different patterns may have significantly different optimum source maps and different optimum OPC solution paths. Consequently, the need for finding a globally optimal OPC solution becomes important. In this work, we introduce a holistic solution including source and mask optimization (SMO), MB-SRAF, conventional OPC and Co-Optimization OPC, in which each technique plays a unique role in process window enhancement: SMO optimizes the source to find the best source solution for all critical patterns; Co-Optimization provides the optimized location and size of scattering bars and guides the optimized OPC solution; MB-SRAF and MB-OPC then utilizes all information from advanced solvers and performs a globally optimized production solution.

Sub-resolution assist feature (SRAF) printing prediction using logistic regression

In optical proximity correction (OPC), the sub-resolution assist feature (SRAF) has been used to enhance the process window of main structures. However, the printing of SRAF on wafer is undesirable as this may adversely degrade the overall process yield if it is transferred into the final pattern. A reasonably accurate prediction model is needed during OPC to ensure that the SRAF placement and size have no risk of SRAF printing. Current common practice in OPC is either using the main OPC model or model threshold adjustment (MTA) solution to predict the SRAF printing. This paper studies the feasibility of SRAF printing prediction using logistic regression (LR). Logistic regression is a probabilistic classification model that gives discrete binary outputs after receiving sufficient input variables from SRAF printing conditions. In the application of SRAF printing prediction, the binary outputs can be treated as 1 for SRAFPrinting and 0 for No-SRAF-Printing. The experimental work was performed using a 20nm line/space process layer. The results demonstrate that the accuracy of SRAF printing prediction using LR approach outperforms MTA solution. Overall error rate of as low as calibration 2% and verification 5% was achieved by LR approach compared to calibration 6% and verification 15% for MTA solution. In addition, the performance of LR approach was found to be relatively independent and consistent across different resist image planes compared to MTA solution.

Resist profile aware source mask optimization

In this paper, we present the approach and results of resist profile aware source mask optimization (SMO). In this approach, the cost functions for optimization include the image properties calculated not only from the resist bottom image planes, but also from the top image planes. Consequently, the optimized source and mask shapes are a good balance between the process window for the bottom CD’s, and top CD control to ensure a straight resist profile favorable for the etching process. We built up the flow of resist profile aware SMO and implemented it on a 1× nm node back-end layer. Two best candidate sources, SMO1 and SMO2 were generated from the conventional SMO flow and the resist profile aware SMO flow, respectively. The simulation results indicate that a better resist profile is achieved by SMO2, although it gives rise to a relatively smaller overlapping process window evaluated at the resist bottom. Wafer data including bottom CD measurement for critical pattern clips and cross-sectional SEM images from selected patterns have shown good matching with the simulation results, indicating that resist-profile aware SMO is a feasible approach to optimize the illumination sources for a reasonable bottom CD based process window as well as favorable resist profiles.

Effect of mask 3D and scanner focus difference on OPC modeling and verification

A robust optical proximity correction (OPC) model must include process variation to be effective in volume manufacturing. Often, calibration of an OPC model is based on data from a single scanner. However, scanner and mask three dimension (3D) effects have been found to affect printing performance and OPC model effectiveness [1]. OPC model robustness is improved if the fingerprints of different scanners are matched as closely as possible. Scanner source map or boundary condition variations can cause isolated and dense feature focus differences between different scanners. The scanner used to build a robust OPC model should have a minimum focus difference between isolated and dense features. Mask 3D effects must be included in OPC model building. Even if the design data is the same, mask 3D effects will vary by different advanced blank film stacks and model fitting will lead to different results. In this work, the effects of focus differences between nested and isolated features for OPC model building are quantified. In addition, mask 3D effect contributions to OPC models will also be illustrated. OPC model tolerance to variation is shown using data from multiple scanners and mask topographies and methodologies to optimize OPC models are presented. The data confirms that different absorber thickness, and n and k values, for advanced binary masks will influence the boundary conditions and effect lithographic performance. A thinner absorber demonstrated better CD prediction than thicker blanks in semi-dense and isolated patterns for both CDTP and inverse CDTP. It also shows that the thinner absorber has better inverse linearity in small isolated features, and has much better prediction for large isolated patterns. The generation of OPC models must include variations due to mask material properties and scanner optical variations to provide robust performance in manufacturing.

OPC model prediction capability improvements by accounting for mask 3D-EMF effects

As mask feature sizes have shrunk well below the exposure wavelength, the thin mask of Kirchhoff approximation breaks down and 3D mask effects contribute significantly to the through-focus CD behavior of specific features. While full-chip rigorous 3D mask modeling is not computationally feasible, approximate simulation methods do enable the 3D mask effects to be represented. The use of such approximations improves model prediction capability. This paper will look at a 28nm darkfield and brightfield layer datasets that were calibrated with a Kirchhoff model and with two different 3D-EMF models. Both model calibration accuracy and verification fitness improvements are realized with the use of 3D models.

Effective use of aerial image metrology for calibration of OPC models

The appropriate representation of the photomask in the simulation of wafer lithography processes has been shown to be of vital importance for 14-nm and below [1]. This task is difficult, since accurate optical metrology and physical metrology of the three-dimensional mask structure is not always available. OPC models for wafer patterning comprise representations of the mask, the optics, and the photoresist process. The traditional calibration of these models has involved empirical tuning of model parameters to CD-SEM data from printed photoresist patterns. Such a flow necessarily convolves the resist effects and it has been difficult to reliably obtain mask and optical parameters which are most representative of physical reality due to aliasing effects. In this work, we have undertaken to decouple the mask model from the photoresist process by use of the ZEISS Wafer-Level CD (WLCD) tool based upon aerial image metrology. By measuring the OPC test pattern mask with WLCD, the mask parameters in the OPC model can be tuned directly without interference of resist effects. This work utilized 14-nm,10-nm, and 7-nm node masks, and we demonstrate that the use of such a flow leads to the most predictive overall OPC models, and that the mask parameters resulting from this flow more closely match the expected physical values. More specifically, the mask corner rounding, sidewall angle, and bias values were tuned to the WLCD data instead of the wafer CD SEM data, and resulted in improved predictive capability of the model. Furthermore, other mask variables not traditionally tuned can be verified or tuned by matching simulation to aerial image metrology.

Aerial image metrology for OPC modeling and mask qualification

As nodes become smaller and smaller, the OPC applied to enable these nodes becomes more and more sophisticated. This trend peaks today in curve-linear OPC approaches that are currently starting to appear on the roadmap. With this sophistication of OPC, the mask pattern complexity increases. CD-SEM based mask qualification strategies as they are used today are starting to struggle to provide a precise forecast of the printing behavior of a mask on wafer. An aerial image CD measurement performed on ZEISS Wafer-Level CD system (WLCD) is a complementary approach to mask CD-SEMs to judge the lithographical performance of the mask and its critical production features. The advantage of the aerial image is that it includes all optical effects of the mask such as OPC, SRAF, 3D mask effects, once the image is taken under scanner equivalent illumination conditions. Additionally, it reduces the feature complexity and analyzes the printing relevant CD.

Source mask optimization using 3D mask and compact resist models

Source Mask Optimization (SMO) has played an important role in technology setup and ground rule definition since the 2x nm technology node. While improvements in SMO algorithms have produced higher quality and more consistent results, the accuracy of the overall solution is critically linked to how faithfully the entire patterning system is modeled, from mask down to substrate. Fortunately, modeling technology has continued to advance to provide greater accuracy in modeling 3D mask effects, 3D resist behavior, and resist phenomena. Specifically, the Domain Decomposition Method (DDM) approximates the 3D mask response as a superposition of edge-responses.1 The DDM can be applied to a sectorized illumination source based on Hybrid-Hopkins Abbe approximation,2 which provides an accurate and fast solution for the modeling of 3D mask effects and has been widely used in OPC modeling. The implementation of DDM in the SMO flow, however, is more challenging because the shape and intensity of the source, unlike the case in OPC modeling, is evolving along the optimization path. As a result, it gets more complicated. It is accepted that inadequate pupil sectorization results in reduced accuracy in any application, however in SMO the required uniformity and density of pupil sampling is higher than typical OPC and modeling cases. In this paper, we describe a novel method to implement DDM in the SMO flow. The source sectorization is defined by following the universal pixel sizes used in SMO. Fast algorithms are developed to enable computation of edge signals from each fine pixel of the source. In this case, each pixel has accurate information to describe its contribution to imaging and the overall objective function. A more continuous angular spectrum from 3D mask scattering is thus captured, leading to accurate modeling of 3D mask effects throughout source optimization. This method is applied on a 2x nm middle-of-line layer test case. The impact of the 3D mask model accuracy on the source profile and corresponding lithographic performance is studied in detail. Furthermore, the impact of using a compact resist model in SMO is also investigated by using the same test case.

Layer aware source mask target optimization

In this paper, we present the approach and results of layer-aware source mask target optimization. In this approach, the design target is co-optimized during source mask optimization (SMO) by considering inter-layer constraints. We tested the method on a 2x nm node metal layer by using both standard and customized cost functions for source optimization. Variable targets were defined for two process window limiting critical pattern cells, with contact-to-metal and metal-tovia coverage rules taken into consideration. The results indicate that layer-aware source mask target optimization gives consistent process window improvement over conventional SMO. The optimized targets prove to be a good balance between lithography process window and post-etch inter-layer coverage margin.