Layout Decomposition for Quadruple Patterning Lithography and Beyond

For next-generation technology nodes, multiple patterning lithography (MPL) has emerged as a key solution, e.g., triple patterning lithography (TPL) for 14/11nm, and quadruple patterning lithography (QPL) for sub-10nm. In this paper, we propose a generic and robust layout decomposition framework for QPL, which can be further extended to handle any general K-patterning lithography (K$>$4). Our framework is based on the semidefinite programming (SDP) formulation with novel coloring encoding. Meanwhile, we propose fast yet effective coloring assignment and achieve significant speedup. To our best knowledge, this is the first work on the general multiple patterning lithography layout decomposition.Comment: DAC'201

Reduced basis method for source mask optimization

Image modeling and simulation are critical to extending the limits of leading edge lithography technologies used for IC making. Simultaneous source mask optimization (SMO) has become an important objective in the field of computational lithography. SMO is considered essential to extending immersion lithography beyond the 45nm node. However, SMO is computationally extremely challenging and time-consuming. The key challenges are due to run time vs. accuracy tradeoffs of the imaging models used for the computational lithography. We present a new technique to be incorporated in the SMO flow. This new approach is based on the reduced basis method (RBM) applied to the simulation of light transmission through the lithography masks. It provides a rigorous approximation to the exact lithographical problem, based on fully vectorial Maxwell's equations. Using the reduced basis method, the optimization process is divided into an offline and an online steps. In the offline step, a RBM model with variable geometrical parameters is built self-adaptively and using a Finite Element (FEM) based solver. In the online step, the RBM model can be solved very fast for arbitrary illumination and geometrical parameters, such as dimensions of OPC features, line widths, etc. This approach dramatically reduces computational costs of the optimization procedure while providing accuracy superior to the approaches involving simplified mask models. RBM furthermore provides rigorous error estimators, which assure the quality and reliability of the reduced basis solutions. We apply the reduced basis method to a 3D SMO example. We quantify performance, computational costs and accuracy of our method.Comment: BACUS Photomask Technology 201

Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm

As the feature size of semiconductor technology shrinks to 10 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry. In this paper, we model the layout decomposition of MPL as a generalized graph coloring problem, which is addressed by a distribution evolutionary algorithm based on a population of probabilistic model (DEA-PPM). DEA-PPM can strike a balance between decomposition results and running time, being scalable for varied settings of mask number and lithography resolution. Due to its robustness of decomposition results, this could be an alternative technique for multiple patterning layout decomposition in next-generation technology nodes

DSA-aware multiple patterning for the manufacturing of vias: Connections to graph coloring problems, IP formulations, and numerical experiments

In this paper, we investigate the manufacturing of vias in integrated circuits with a new technology combining lithography and Directed Self Assembly (DSA). Optimizing the production time and costs in this new process entails minimizing the number of lithography steps, which constitutes a generalization of graph coloring. We develop integer programming formulations for several variants of interest in the industry, and then study the computational performance of our formulations on true industrial instances. We show that the best integer programming formulation achieves good computational performance, and indicate potential directions to further speed-up computational time and develop exact approaches feasible for production

Direct-Write Ion Beam Lithography

Patterning with a focused ion beam (FIB) is an extremely versatile fabrication process that can be used to create microscale and nanoscale designs on the surface of practically any solid sample material. Based on the type of ion-sample interaction utilized, FIB-based manufacturing can be both subtractive and additive, even in the same processing step. Indeed, the capability of easily creating three-dimensional patterns and shaping objects by milling and deposition is probably the most recognized feature of ion beam lithography (IBL) and micromachining. However, there exist several other techniques, such as ion implantation- and ion damage-based patterning and surface functionalization types of processes that have emerged as valuable additions to the nanofabrication toolkit and that are less widely known. While fabrication throughput, in general, is arguably low due to the serial nature of the direct-writing process, speed is not necessarily a problem in these IBL applications that work with small ion doses. Here we provide a comprehensive review of ion beam lithography in general and a practical guide to the individual IBL techniques developed to date. Special attention is given to applications in nanofabrication