Technical Papers

One of the challenges associated with shrinking design dimensions is finding photomask inspection settings which achieve sufficient defect detection capabilities while supporting aggressive Optical Proximity Correction (OPC). The most recent technology nodes require very aggressive and advanced Resolution Enhancement Techniques (RETs) which involve printing small features that are challenging for mask inspection tools. We examine the problems associated with constraining Models-Based OPC with mask inspection driven rules. We give examples of a 45nm technology node contact layer design which will receive sub-optimal OPC treatment due to mask inspection constraints. We then take the mask defect specification typically used for this mask layer, and use Monte Carlo simulation methods to place minimum sized simulated defects in various locations in close proximity to these sensitive layouts.

Simulations of the optimal OPC are compared to optimal OPC with defects, and to the sub-optimal constrained OPC. Using knowledge about the frequency of small defects on masks, one can compare the risks associated with small mask defects to the risks associated with sub-optimal OPC. This exercise demonstrates that there are some instances where mask rules based on inspection capabilities and defect sensitivity alone can be problematic, and that OPC requirements need to be taken into account when choosing a defect specification and an inspection strategy. We conclude by proposing a strategy for balancing these requirements in a practical manner.

The OPC treatment of aerial mage ripples (local variations in aerial contour relative to constant target edges) is one of the growing issues with very low-k1 lithography employing hard off-axis illumination. The maxima and minima points in the aerial image, if not optimally treated within the existing model based OPC methodologies, could induce severe necking or bridging in the printed layout.

The current fragmentation schemes and the subsequent site simulations are rule-based, and hence not optimized according to the aerial image profile at key points. The authors are primarily exploring more automated software methods to detect the location of the ripple peaks as well as implementing a simplified implementation strategy that is less costly. We define this to be an adaptive site placement methodology based on aerial image ripples.

Recently, the phenomenon of aerial image ripples was considered within the analysis of the lithography process for cutting-edge technologies such as chromeless phase shifting masks and strong off-axis illumination approaches. Effort is spent during the process development of conventional model-based OPC with the mere goal of locating these troublesome points. This process leads to longer development cycles and so far only partial success was reported in suppressing them (the causality of ripple occurrence has not yet fully been explored). We present here our success in the implementation of a more flexible model-based OPC solution that will dynamically locate these ripples based on the local aerial image profile nearby the features edges. This model-based dynamic tracking of ripples will cut down some time in the OPC code development phase and avoid specifying some rule-based recipes.

Our implementation will include classification of the ripples bumps within one edge and the allocation of different weights in the OPC solution. This results in a new strategy of adapting site locations and OPC shifts of edge fragments to avoid any aggressive correction that may lead to increasing the ripples or propagating them to a new location. More advanced adaptation will be the ripples-aware fragmentation as a second control knob, beside the automated site placement.

Model-Based Optical Proximity Correction (MBOPC) is now found in nearly all resolution enhancement recipes used in leading technology integrated circuit fabrication facilities. Many masks now have critical dimensions less than the exposure wavelength, which results in light diffraction that distorts the image projected onto the wafer. The industry is relying more and more on MBOPC to compensate for optical effects that are induced during the exposure of these masks. The MBOPC operation is usually the highest computational time contributor in the RET flow. MBOPC procedures include the fragmentation of layout edges longer than a specific value into a number of subedges(fragments).

The software engine can move and manipulate each fragment to improve the image transferred to the wafer. In the sparse MBOPC approach, each fragment receives one or more optical simulation sites, which is a one-dimensional array of points where light intensity is sampled and calculated. To correctly capture the resist behavior at each simulation site, there must be enough points to ensure extension of the site to a certain distance from the fragment. Adding more points beyond this distance does not add any benefit, but can significantly increase the runtime.

This paper presents an automated method that analyzes layouts for different technology nodes that depend on sparse simulations as their MBOPC engine, and reports the optimized number of simulation points that need to be in the
simulation site to get the desired accuracy and optimum runtime performance.

As the technology shrinks toward 65nm technology and beyond, Optical Proximity Correction (OPC) becomes more important to insure proper printability of high-performance integrated circuits. This correction involves some geometrical modifications to the mask polygons to account for light diffraction and etch biasing. Model-based OPC has proven to be a convenient, accurate, and efficient methodology. In this method, raw calibration data are measured from the process. These data are used to build a VT5 resist model [1] that accounts for all proximity effects that attendant to the lithography process. To ensure the reliability of the calibrated VT5 model, these data must be broad in the image parameter space (IPS) to account for different one-dimensional and two-dimensional features for the design intent. Failure to provide sufficient IPS (i.e. mimic the design intent) coverage during model calibration could result in marginalizing the VT5 model during OPC, but is difficult to judge when there is enough data volume to safely interpolate and extrapolate design intent. In this paper we introduce a new metric called Safe Interpolation Distance(SID). This metric is a multi-dimensional metric which can be used to automatically detect the portions of the target design that are not covered well by the desired VT5 model.

Sub-resolution Assist Feature (SRAF) insertion is one of the most important Resolution Enhancement Techniques (RET) for the 65 nm, 45 nm nodes and beyond. In this paper, we are proposing a novel approach for the optimum placement of 2D SRAF structures using state of the art Calibre RET flow. In this approach, the optimal SRAF shapes are achieved simultaneously during the OPC step. The SRAF and main features are optimized to account for their edge placement and process window metrics (aerial image slope/contrast, out of focus/dose EPE, etc…). The resulting mask shapes deliver some of the properties that can be obtained using the Inverse Lithography Techniques (ILT), such as excellent Process Window Performance, while there is almost no impact on the runtime. The implemented model-based optimization flow remains compatible with the current OPC production flows.

To maximize the process window and CD control of main features, sizing and placement rules for sub-resolution assist features (SRAF) need to be optimized, subject to the constraint that the SRAFs not print through the process window. With continuously shrinking target dimensions, generation of traditional rule-based SRAFs is becoming an expensive process in terms of time, cost and complexity. This has created an interest in other rule optimization methodologies, such as image contrast and other edge- and image-based objective functions. In this paper, we propose using an automated model-based flow to obtain the optimal SRAF insertion rules for a design and reduce the time and effort required to define the best rules. In this automated flow, SRAF placement is optimized by iteratively generating the space-width rules and assessing their performance against process variability metrics. Multiple metrics are used in the flow. Process variability (PV) band thickness is a good indicator of the process window enhancement. Depth of focus (DOF), the total range of focus that can be tolerated, is also a highly descriptive metric for the effectiveness of the sizing and placement rules generated. Finally, scatter bar (SB) printing margin calculations assess the allowed exposure range that prevents scatter bars from printing on the wafer.