Benjamin D. Painter

Model-based assist feature placement using inverse imaging approach

The continuing reduction in feature dimensions and tightening of process constraints have led to an increasing demand for model-based approaches, which can efficiently explore the AF solution space, and achieve AF configurations not easily accessible via rules. In this work, we approach the AF placement problem as an inverse imaging problem. We discuss the generation of an inverse mask field and its use in determining the assist feature location. The results are compared with the single iteration intensity-field based AF placement with regard to symmetry, speed, memory, convergence, and accuracy. Several results with different pitches and illumination conditions are presented to demonstrate the robustness and adaptability of the inverse mask AF placement.

Classical control theory applied to OPC correction segment convergence

Model based Optical Proximity Correction work is currently performed by segmenting patterns in a layout and iteratively applying corrections to these segments for a set number of iterations. This is an open loop control methodology that relies on a finely tuned algorithm to arrive at a proper correction. A goal of this algorithm is to converge in the fewest number of iterations possible. As technology nodes become smaller, different correction areas tend to correct at different rates, and these correction rates are diverging with process node. This leads to more iterations being required to converge to a final OPC solution, the consequence of which is an increased runtime and tapeout cost. The current solution to this problem is to use proportional damping factors to attempt to bring different structure types to a solution. Classical control theory provides tools to optimize the convergence of these processes and to speed up convergence in physical systems. Introducing derivative and integral control while continuing use of proportional control should reduce the number of iterations needed to converge to a final solution as well as optimize the convergence for varied configurations.

Model-based placement and optimization of subresolution assist features

Sub-resolution assist features (SRAFs) are an important tool for improving through-process robustness of advanced lithographic processes. Assist features have generally been placed and adjusted according to heuristic rules. The complexity of these rules increases rapidly with shrinking features size requiring more wafer data for calibration and more effort on the part of engineers. For advanced nodes, a model-based approach may better account for the variety of two-dimensional geometries and reduce substantially the amount of user effort required for effective SRAF placement. There are many ways in which model-based methods can be used to improve the effectiveness of assist features; we investigate several here. In the investigations described here, process window models may be employed to: 1) derive optimal rules for initial AF placement in a rule-based process, 2) resolve mask rule violations in optimal ways, and 3) make post-placement corrections of mask sites with poor behavior. In addition, we discuss a method for replacing an initial rule-based assist feature placement with a model-based placement which can consider the local two-dimensional geometry.

Improvements in model-based assist feature placement algorithms

Demanding process window constraints have increased the need for effective assist feature placement algorithms that are robust and flexible. These algorithms must also allow for quick ramp up when changing nodes or illumination conditions. Placement based on the optical components of real process models has the potential to satisfy all of these requirements. We present enhancements to model-based assist feature algorithms. These enhancements include exploration of image-processing techniques that can be exploited for contact-via AF placement, model-based mask rule check (MRC) conflict resolution, the application of models to line-space patterns, and a novel placement technique for contact-via layers using a specially-built single modeling kernel.

Assist feature placement analysis using focus sensitivity models

Sub-Resolution Assist Features (SRAFs) are placed into patterns to enhance the through process imaging performance of critical features. SRAFs are typically placed using complex rules to achieve optimal configurations for a pattern. However, as manufacturing process nodes are growing increasingly complex, the SRAF placement rules will most likely be unable to produce optimal performance on some critical features. A primary impediment to resolving these problems is identifying poorly performing features in an efficient manner. A new process model form referred to as a Focus Sensitivity Model (FSM) is capable of rapidly analyzing SRAF placement for through process pattern performance. This study will demonstrate that an FSM is capable of finding suboptimal SRAF placements as well as missing SRAFs. In addition, the study suggests that the FSM does not need to comprehend the entire photolithography process to analyze SRAF placement. This results in simpler models that can be generated before a manufacturing process enters its development phase.

A methodology to calculate line-end correction feature performance as a function of reticle cost

Mask fabrication costs are significantly aggravated by OPC complexity. This increased complexity is presumably needed to accurately render 2-D configurations. The humble line-end is one of the most difficult 2-D configurations to print accurately, when considering process margin requirements and mask fabrication constraints. In this paper, the requirements for proximity corrected line-end structures will be explored and a pattern complexity metric will be proposed to compare relative mask cost versus line-end lithographic performance. Many types of correction shapes are available to improve process margin for line-ends. However, the cost of producing these various line-end configurations can vary dramatically. Using both a simple optical model to simulate line-end performance through focus offset and a cost metric based on fracture shots, a comparison of six types of lines ends for correction and process efficiency will be undertaken. Each of the six line-end corrections will attempt to produce equally effective silicon line-end shapes. Line-ends will be evaluated based on shortening (pullback), pinching, and bridging characteristics. Line-end lithographic behavior will be characterized through all process window boundary conditions. The objective of this study is to quantify the tradeoffs among three variables: mask cost, process-window robustness, and design tolerance margin. In addition, through the study of proximity effects on the various line-end types, the possibility of mixing expensive but high performance line-ends with simpler less aggressive line-ends to reduce reticle cost while maintaining or increasing correction fidelity will be studied.

Integrating assist feature print fixing with OPC

A challenge in model-based assist feature placement is to find optimal placements while satisfying mask rules and preventing AF printing. There are numerous strategies for achieving this ranging from fully rule-based methods to pixel-based inversion. Our proposed solution is to identify the optimal locations of assist features using modeling information based strictly on optics and resist stack optical characteristics. Once these positions have been found, preliminary AFs can be placed. At this point suggested sizes and shapes can be identified, although these can later be modified. In a later step, MRC cleanup, printability fixing, and main-pattern OPC can be performed simultaneously. This has the advantage of allowing the use of the full process model to predict the location of OPC edges accurately, and use calibrated or 3d mask models to determine assist feature printing behavior. This correction is done while maintaining MRC constraints. In this flow, an AF placement field, generated from the pre-OPC target patterns, can be used to provide accurate guidance on how to move assist features to get the most benefit while keeping other constraints in mind. Using this method, a range of printability fixing strategies, guided by placement benefits, is available. We present data showing that the benefit of AF placements can be determined from optical parameters, on target (non-OPC) data, and that this method leads to beneficial yet compliant masks.

Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology

Inverse imaging has been long known to provide a true mathematical solution to the mask design problem. However, it is often times marred by problems like high run-time, mask manufacturability costs, and non-invertible models. In this paper, we propose a mask synthesis flow for advanced lithography nodes, which capitalizes on the inverse mask solution while still overcoming all the above problems. Our technique uses inverse mask technology (IMT) to calculate an inverse mask field containing all the useful information about the AF solution. This field is fed to a polygon placement algorithm to obtain initial AF placements, which are then cooptimized with the main features during an OPC/AF print-fix routine to obtain the final mask solution. The proposed flow enables process window maximization via IMT while guaranteeing fully MRC compliant masks. We present several results demonstrating the superiority of this approach. We also compare our IMT-AFs with the best AF solution obtained using extensive brute-force search (via a first principles simulator, S-litho), and prove that our solution is optimum.

Applying assist features to improve two dimensional feature process robustness

Sub-resolution assist features are an important tool for improving process robustness for one-dimensional pattern features at advanced manufacturing process nodes. However, sub-resolution assist feature development efforts have not generally considered optimization for process robustness with two-dimensional pattern features. This generally arises both from conservatively placing SRAFs to avoid the possibility of imaging, and from a desire to simplify SRAF placement rules. By studying two-dimensional features using a manufacturing sensitivity model, one can gain insight into the capabilities of SRAFs regarding two-dimensional pattern features. These insights suggest new methodologies for shaping assist features to enhance two-dimensional feature robustness. In addition, a manufacturing sensitivity model form can be employed to optimize the placement of multiple competing SRAFs in localized two-dimensional regions. Initial studies demonstrate significant pullback reduction for two-dimensional features once SRAF placement has been optimized using the manufacturing sensitivity model form.

Correlation of experimentally measured atomic scale properties of EUV photoresist to modeling performance: an exploration

Extreme ultraviolet (EUV) lithography at 13.5 nm stands at the crossroads of next generation patterning technology for high volume manufacturing of integrated circuits. Photo resist models that form the part of overall pattern transform model for lithography play a vital role in supporting this effort. The physics and chemistry of these resists must be understood to enable the construction of accurate models for EUV Optical Proximity Correction (OPC). In this study, we explore the possibility of improving EUV photo-resist models by directly correlating the parameters obtained from experimentally measured atomic scale physical properties; namely, the effect of interaction of EUV photons with photo acid generators in standard chemically amplified EUV photoresist, and associated electron energy loss events. Atomic scale physical properties will be inferred from the measurements carried out in Electron Resist Interaction Chamber (ERIC). This study will use measured physical parameters to establish a relationship with lithographically important properties, such as line edge roughness and CD variation. The data gathered from these measurements is used to construct OPC models of the resist.