Model Based Mask Process Correction (MB-MPC) has been deployed in the photomask manufacturing process for almost a decade. It has now become a must have process for leading edge masks that require high level manufacturing accuracy. Recently, aggressive OPC methods such as ILT have significantly increased the complexity of mask data. This impacts Mask Data Preparation’s (MDP) processing time due to large mask data volumes. By its nature, MB-MPC process is quite time-consuming since it needs to perform complex calculations repeatedly, and so it takes the largest part of the total MDP time. This puts high pressure on turn-around time (TAT) reduction without losing accuracy and necessitates the need to develop algorithms that can operate on tight TAT budgets. Pattern Matching (PM) approach could be used to mitigate high processing times of MB-MPC by leveraging inherent repetitiveness of real-world mask data. Since a pattern simulation result is influenced by all patterns located within the mask model radius, to consider one pattern as a repetition of another, the central pattern as well as the neighborhood must match. This method is called Neighborhood Pattern Matching (NPM). In this paper, we evaluate the effectiveness of NPM when applied to the MB-MPC software developed by Synopsys. First, we introduce the fundamental concepts of NPM. Then we validate the algorithm with test patterns to evaluate its behavior. Finally, we measure processing time with several types of device patterns and confirm how NPM can reduce MPC calculation time on real mask data.
Mask Process Correction (MPC) is becoming increasingly relevant as the industry moves toward more challenging technology nodes. Because running MPC on large layouts can be extremely resource intensive, it is important to strike a balance between the quality of the correction and the total turnaround time (TAT). This paper describes the results of applying a geometry-based MPC solution to a mask lithography process created at Toppan where the model is calibrated from AEI metrology data of patterns that accounts for beam blur, etch, and proximity effects present in the etched mask up to ~1 um. In this solution, the model calibration can result in different but equivalent predictors, i.e., the model parameters can differ while the overall error residuals (model RMS) can be nearly identical. The following sections probe a possible trade-off between correction quality and speed by testing how an MPC software based on edge movement behaves as the effective range of an enhanced multi-Gaussian mask model template is constrained.
With the shrinking of devices, aggressive OPC is becoming imperative. Generally, OPC must be kept within photomask
manufacturing limits, but at process and OPC development stages, patterns exceeding photomask manufacturing and
inspection limits are often included. To resolve this issue, MRC (Mask Rule Checking) is executed as a method to verify
patterns exceeding photomask manufacturing and inspection limits. Two options are available in MRC to solve errors:
(1) repair layout (or OPC) of corresponding areas; or (2) manufacture photomask including corresponding areas but with
no inspection. Option (1) is generally extremely time-consuming, and if lithographically feasible, (2) would be selected.
However, if detected error flags become massive, it is nearly beyond human control to take care of configurations of
DNIR(Do Not Inspect Region). In addition, massive amounts of DNIR will augment inspection tool setup time almost
factorial. Further, inspection tools have limitations in DNIR setup method, and DNIR settings that do not meet criteria
will be considered as setting violations. Therefore, we developed TLDD (Toppan Layout Driven DNIRs), a tool that
automatically generates DNIR based on detected by MRC. This tool has the following features: (1) applies limitations to
the number of DNIRs; (2) follows DNIR limitations of inspection tools; and (3) follows both (1) and (2) upon which
DNIR area is minimized as much as possible. By utilizing this tool, difficult-to-inspect regions can be automatically set
as DNIR independent of DNIR rules of inspection tools or individual operator skills, while enabling inspection of
important areas at high sensitivity.
In recent years, mask critical dimension (CD) linearity and uniformity has become increasingly important. The ITRS roadmap shows the mask CD control requirements exceeding those of the wafer side beyond the 45nm node. Measurements show that there are systematic, uncorrected proximity effects even when a state-of-the-art proximity effect correction (PEC) algorithm is used. The uncorrected proximity effect is predictable with a computational model. The model for e-beam lithography and etch process contains terms to model short-range pattern density effects and plasma shadowing effect in Cr-etch. The model is calibrated using CD measurements on a test mask. The model is valid for arbitrary 2-D patterns. We present a model-based mask process compensation (MPC) method which applies geometric changes to polygons as in OPC. We discuss the goodness of model fit to the calibration data; verification of the calibrated model by SEM images; and the improvement obtained by MPC. The mask writing error, i.e. final inspection CD minus incoming database CD, was reduced by a factor of 2 through the use of MPC.