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Multiple patterning layout decomposition (MPLD) has been widely investigated, but so far there is no decomposer that dominates others in terms of both result quality and efficiency. This observation motivates us to explore how to adaptively select the most suitable MPLD strategy for a given layout graph, which is nontrivial and still an open problem. In this article, we propose a layout decomposition framework based on graph convolutional networks to obtain the graph embeddings of the layout. The graph embeddings are used for graph library construction, decomposer selection, graph matching, stitch removal prediction, and graph coloring. In addition, we design a fast nonstitch layout decomposition algorithm that purely depends on the message passing graph neural network. The experimental results show that our graph embedding-based framework can achieve optimal decompositions in the widely used benchmark with a significant runtime drop even compared with fast but nonoptimal heuristics. [ABSTRACT FROM AUTHOR]

Multiple patterning lithography has been widely adopted in advanced technology nodes of VLSI manufacturing. As a key step in the design flow, multiple patterning layout decomposition (MPLD) is critical to design closure. Due to the $\mathcal {N} \mathcal {P} $ -hardness of the general decomposition problem, various efficient algorithms have been proposed with high-quality solutions. However, with increasingly complicated design flow and peripheral processing steps, developing a high-quality layout decomposer becomes more and more difficult, slowing down further advancement in this field. This article presents $\mathsf {OpenMPL}$ (2020), an open-source layout decomposition framework, with well-separated peripheral processing and core solving steps. Besides, previous algorithms or techniques are inspected and several issues are discovered. We then propose corresponding new algorithms to resolve these issues. The experiments demonstrate the effectiveness of our proposed algorithms and the efficiency of $\mathsf {OpenMPL}$. [ABSTRACT FROM AUTHOR]

Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing process in VLSI manufactures. The main challenge of dummy filling is balancing multiple objectives, such as fill amounts, planarity, parasitic capacitance, etc. An obvious drawback of traditional rule-based dummy filling methods is pattern densities, instead of post-chemical mechanical polishing (CMP) topographies, being included in optimization objectives. Although the quality of post-CMP topography strongly depends on pattern features of layouts, especially the density uniformity, however, experimental results show that chip surface variations are not exactly the same as density variations. In this article, a unified dummy fill insertion optimization framework is proposed, integrated with the multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver, where all objectives are considered without approximation. Inside this framework, a full-chip CMP simulator is first integrated to evaluate the planarity of the chip surface. By selecting the initial points smartly with heuristic prior knowledge, the proposed method can be effectively accelerated. The effectiveness of the proposed algorithm is verified with the average 25.8% improvement of quality compared with rule-based methods. [ABSTRACT FROM AUTHOR]

As the integrated circuits (ICs) technology continues to scale, resolution enhancement techniques (RETs) are mandatory to obtain high manufacturing quality and yield. Among various RETs, subresolution assist feature (SRAF) generation is a key technique to improve the target pattern quality and lithographic process window. While model-based SRAF insertion techniques have demonstrated high accuracy, they usually suffer from high computational cost. Therefore, more efficient techniques that can achieve high accuracy while reducing runtime are in strong demand. In this article, we leverage the recent advancement in machine learning for image generation to tackle the SRAF insertion problem. In particular, we propose a new SRAF insertion framework, GAN-SRAF, which uses generative adversarial networks (GANs) to generate SRAFs directly for any given layout. Our proposed approach incorporates a novel layout to image encoding using multichannel heatmaps to preserve the layout information and facilitate layout reconstruction. Our experimental results demonstrate ${\sim }14.6\times $ reduction in runtime when compared to the previous best machine learning approach for SRAF generation, and ${\sim }144\times $ reduction compared to the model-based approach, while achieving comparable quality of results. [ABSTRACT FROM AUTHOR]

Lithography simulation is computationally expensive for hotspot detection. Machine learning-based hotspot detection is a promising technique to reduce the simulation overhead. However, most learning approaches rely on a large amount of training data to achieve good accuracy and generality. At the early stage of developing a new technology node, the amount of data with labeled hotspots or nonhotspots is very limited. In this paper, we propose a semisupervised hotspot detection with self-paced multitask learning paradigm, leveraging both data samples with/without labels to improve model accuracy and generality. Experimental results demonstrate that our approach can achieve 4.6%–6.5% better accuracy at the same false alarm levels than the state-of-the-art work using 10%–50% of training data. [ABSTRACT FROM AUTHOR]

Quantum computing has raised great interests for its potential to achieve an asymptotic speedup on specific problems. Current quantum devices suffer from noise which needs robust and scalable error-correcting schemes. Topological quantum error correction (TQEC) is among the most promising error-correcting techniques with exponential suppression of error with linear increase of space-time complexity. In this paper, we present the first work to explore space-time optimization between 1-D and 2-D architectures for TQEC circuits. We prove the NP-hardness of the qubit routing problem in the layout synthesis and propose an efficient algorithm to optimize space-time volumes for both 1-D and 2-D qubit architectures with promising experimental results. [ABSTRACT FROM AUTHOR]

As very large-scale integration technology shrinks to fewer tracks per standard cell, e.g., from 10 to 7.5-track libraries (and lesser for 7 nm), there has been a rapid increase in the usage of multiple-row cells like two- and three-row flip-flops, buffers, etc., for design closure. Additionally, the usage of multibit flip-flops or flop trays to save power creates large cells that further complicate critical design tasks, such as placement. Detailed placement happens to be a key optimization transform, which is repeatedly invoked during the design closure flow to improve design parameters, such as wirelength, timing, and local wiring congestion. Advanced node designs, with hundreds of thousands of multiple-row cells, require a paradigm change for this critical design closure transform. The traditional approach of fixing multiple-row cells during detailed placement and only optimizing the locations of single-row standard cells can no longer obtain appreciable quality of results. It is imperative to have new techniques that can simultaneously optimize both multiple- and single-row height cell locations during detailed placement. In this paper, we propose a new density-aware detailed placer for heterogeneous-sized netlists. Our approach consists of a chain move scheme that generalizes the movement of heterogeneous-sized cells, a nested dynamic programming-based approach for ordered double-row placement and a network flow-based formulation to solve ordered multiple-row placement for wirelength and density optimization. Experimental results demonstrate the effectiveness of these techniques in wirelength minimization and density smoothing compared with the most recent detailed placers for designs with heterogeneous-sized cells. [ABSTRACT FROM PUBLISHER]

Subresolution assist feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model-based and rule-based approaches are widely adopted in the semiconductor industry. The model-based SRAF generation can achieve a high accuracy but it is known to be time-consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. The rule-based SRAF generation is highly technology dependent and it is becoming extremely difficult to render high-quality results in advanced technology nodes. This paper proposes supervised data learning techniques for fast yet consistent SRAF generation with high-quality results. We first propose the constrained concentric circle with area sampling scheme for feature extraction. Illumination source symmetry-based feature compaction technique is further invented to reduce the training data set size and achieve consistent SRAF predictions. Using accurate model-based SRAFs as training data, classification models based on logistic regression (LGR) and support vector machine are calibrated for SRAF predictions. Moreover, the probability maximum prediction is proposed to generate manufacturing-friendly SRAFs with a greedy simplification scheme. We compare support vector machine and LGR models by embedding into an entire mask optimization flow, where the support vector machine model obtains better lithographic performance. Experimental results demonstrate that, compared with the commercial Calibre tool, supervised data learning techniques for SRAF generation obtain significant speed up (>3X for a 100~ \mathrmum ^2 layout clip) and comparable lithographic performance in terms of edge placement error and process variation band. [ABSTRACT FROM AUTHOR]

In deep-submicron very large scale integration manufacturing, dummy fills are widely applied to reduce topographic variations and improve layout pattern uniformity. However, the introduction of dummy fills may impact the wire electrical properties, such as coupling capacitance. Traditional tile-based method for fill insertion usually results in very large number of fills, which increases the cost of layout storage. In advanced technology nodes, solving the tile-based dummy fill design is more and more expensive. In this paper, we propose a high performance dummy fill insertion framework based on geometric properties to optimize multiple objectives simultaneously, including coupling capacitance, density variations and gradient. The experimental results for ICCAD 2014 contest benchmarks demonstrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Triple patterning lithography (TPL) is one of the most promising lithography technology in sub-14-nm technology nodes, especially for complicated low metal layer manufacturing. To overcome the intracell routability problem and improve the cell regularity, recently middle-of-line (MOL) layers are employed in standard cell design. However, MOL layers may introduce a large amount of cross-row TPL conflicts for row-based design. Motivated by this challenge, in this paper we propose the first TPL aware detailed placement toward zero cross-row MOL conflict. In standard cell precoloring, Boolean-based look-up table is proposed to reduce solution space. In detailed placement stage, three powerful techniques, i.e., local reordered single row refinement, min-cost flow-based conflict removal, and local cell interleaving, are proposed to provide zero TPL conflict solution. The experimental results demonstrate the effectiveness of our proposed methodologies. [ABSTRACT FROM AUTHOR]

As the feature size of semiconductor process further scales to sub-16 nm technology node, triple patterning lithography (TPL) has been regarded as one of the most promising lithography candidates along with extreme ultraviolet, electron beam lithography, and directly self-assembly. M1 and contact layers, which are usually deployed within standard cells, are the most critical and complex parts for modern digital designs. Traditional design flow that ignores TPL in early stages may limit the potential to resolve all the TPL conflicts. In this paper, we propose a coherent framework, including standard cell compliance and detailed placement, to enable TPL friendly design. Considering TPL constraints during early design stages, such as standard cell compliance, improves the layout decomposability. With the precoloring solutions of standard cells, we present a TPL aware detailed placement where the layout decomposition and placement can be resolved simultaneously. In addition, we propose a linear dynamic programming to solve TPL aware detailed placement with maximum displacement, which can achieve good trade-off in terms of runtime and performance. Experimental results show that our framework can achieve zero conflict, meanwhile can effectively optimize the stitch number and placement wire-length. [ABSTRACT FROM PUBLISHER]