This paper presents an innovative use of machine learning (ML) to improve etch modeling by integrating monotonic machine learning methods with ML-based contour metrology. Unlike traditional methods that rely on single gauge-based data, our approach leverages comprehensive contour data extracted from SEM images to predict etching biases. It handles large datasets efficiently and adapts dynamically to new data. A primary element of our strategy involves constructing a retargeting layer with etch bias, derived from features at multiple sites or points of interest (POIs) on a reference layer which is generated with a fuzzy clustering model. These features and their corresponding etch biases serve as training data for our semi-supervised model which will be used for prediction on large scale designs.
Siemens EDA (Mentor) has published their pioneering work on matrix OPC at SPIE before, in the same title but part I and II. Based on this work, an OPC feature MatrixOPC has been developed at Siemens EDA (Mentor). The MatrixOPC feature is now used by customers in production recipes routinely. However, this work was only focused on rectilinear OPC or Manhattan masks. In this paper, we present our current effort in generalizing the rectilinear matrix OPC to the curvilinear mask setting and to curvilinear OPC. Our initial test with a particular test case shows a promise that the new version, curvilinear matrix OPC and still under development, may also become a useful supplemental instrument for our curvilinear OPC solutions, compared to the curvilinear OPC practices without it. In this paper we will define the Jacobian matrix for the curvilinear mask setting, and compare the Jacobian matrices obtained from the brute-force definition and from our fast approximation algorithm, by comparing their total differentials. We also compare the OPC results from regular curvilinear OPC and matrix OPC with a fast approximated Jacobian.
Curvilinear(CL) mask shapes have showed better lithography performance, including improved process window, better PVband and MEEF compared to Manhattan mask. With the development of Multi-Beam-Mask-Writer (MBMW), the adoption of CL mask in production becomes reality.
However, there are multiple challenges associated with CL data, such as complex mask shape and large data volume. One of the most important challenges is to have a good set of Mask-Rule-Check(MRC) rules which is essential to achieve good OPC mask quality.
Calibre® OPCVerify has been developed for years to check CL shapes. Combining with existing checks, a full suite of CL MRC checks has been added. In this paper, we will present a fully integrated CL verification flow.
Sub-resolution assist features (SRAFs) have become an integral part of low-k lithography’s resolution enhancement techniques (RET). Gradually maturing EUV technology indicates that SRAF insertion might be necessary for 5nm technology nodes and below.
In mask synthesis flows, during the correction step, an SRAF print avoidance (SPA) algorithm is relying on detection of printing predicted by model based simulation. In this paper we are presenting a cross-MEEF based SPA approach that offers elimination of SRAF printing while minimizing impact on process window.
Process window OPC (PWOPC) is widely used in advanced technology nodes as one of the most important resolution enhancement techniques (RET).1 PWOPC needs to consider not only edge placement error (EPE) from nominal condition simulations, but also constraints based on process variation simulations, such as pinch and bridge related requirements based on process variation band (PVBAND). Those constraints can be challenging to meet as feature size continues to shrink in advanced nodes.
In this paper a novel matrix retargeting based PWOPC was developed to find optimal OPC solutions by solving constraints-based matrix and applying minimal retargeting as needed.2 Experiment results showed enhanced process window and reasonable performance.
We present an optimization methodology for the template designs of subresolution contacts using directed self-assembly (DSA) with graphoepitaxy and immersion lithography. We demonstrate the flow using a 60-nm-pitch contact design in doublet with Monte Carlo simulations for DSA. We introduce the notion of template error enhancement factor (TEEF) to gauge the sensitivity of DSA printing infidelity to template printing infidelity and evaluate optimized template designs with TEEF metrics. Our data show that source mask optimization and inverse lithography technology are critical to achieve sub-80 nm non-L0 pitches for DSA patterns using 193i.
In this paper, we present an optimization methodology for the template designs of sub-resolution contacts using directed self-assembly (DSA) with grapho-epitaxy and immersion lithography. We demonstrate the flow using a 60nm-pitch contact design in doublet with Monte Carlo simulations for DSA. We introduce the notion of Template Error Enhancement Factor (TEEF) to gauge the sensitivity of DSA printing infidelity to template printing infidelity, and evaluate optimized template designs with TEEF metrics. Our data shows that SMO is critical to achieve sub-80nm non- L0 pitches for DSA patterns using 193i.
In the traditional OPC (Optical Proximity Correction) procedure, edges in a layout are broken into fragments and each fragment is iteratively adjusted by multiplying its EPE (Edge Placement Error) with a carefully selected or calculated feedback. However, the ever-shrinking technology nodes in recent years bring stronger fragment to fragment interaction. The feedback tuning approach driven by a single fragment EPE is no longer sufficient to achieve good pattern fidelity with reasonable turn-around-time. Various novel techniques such as matrix OPC [1, 2] have been developed in the past to incorporate the influence of neighboring fragments into each fragment’s movement. Here we introduce a neighboraware feedback controller for full chip level OPC applications, following the concept and algorithms of the matrix OPC that were laid out in Cobb and Granik’s work [1]. We present experimental results and discuss the benefits and challenges of the proposed feedback controller.
Optical Proximity Correction (OPC) can be formulated as a constrained optimization problem. The constraints are mask constraint rules for space and width. These are sometimes called Mask Rule Checks (MRC), or Design Rule Checks (DRC). At 90nm and below, intelligent constraint handling is required for good OPC. In this paper, we show a technique for OPC constraint checking which is built in to the OPC feedback algorithm. The system is flexible enough to allow relaxed rules for corner-to-corner checking versus edge-to-edge checking. Also, the system can categorize checks by the length of the edges being compared. Lastly, the system can create special checks from line-ends to other features, or any user-defined edge type to any other user-defined edge type. In addition, we present a method for multiple layer enclosure rules which can be used for multiple exposure OPC. These enclosure constraints are useful for assurance of overlay tolerance.
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