The photonic integrated circuit (PIC) is a promising direction for future computing and interconnect, which involves many curvilinear geometries to modulate and transmit signals. To ensure the functionality, the PIC manufacturing requires very meticulous optimization to refrain from geometry distortion resulting from the lithography process. While conventional optical proximity correction (OPC) methods can handle curvilinear features, they face challenges in mask manufacturability, computational cost, and the ability to correct any-angle edge placement error (EPE). This paper proposes BEAM, a native framework designed for photonic designs with curvilinear patterns, including lossless curvilinear pattern representation and a powerful OPC solver. BEAM uses control points to represent curvilinear mask shapes directly, avoiding Manhattanization and approximation errors. Instead of manually specifying movement directions, control points are bidirectionally updated along two orthogonal basis directions, ensuring versatile corrections. To further enhance efficiency, we propose a fast batch-based sensitivity measurement strategy that effectively guides the movement of control points while substantially reducing the computational overhead. The effectiveness of BEAM is demonstrated on multiple fundamental layout components of photonic designs, achieving state-of-the-art correction performance in terms of mask quality and computational efficiency.

Inferring latent interaction structures from observational time series is a fundamental yet challenging problem in dynamical systems. Existing deep learning methods employ unidirectional information aggregation via incoming edges, failing to identify the mutual dependencies prevalent in real dynamics as well as the feedback effects induced by sampling intervals, which leads to inferential bias. To address this, we propose the D ual- A ttention R elational I nference (DARI), a framework designed to learn latent interaction structures from dynamical observations. DARI employs a coupled bidirectional attention mechanism to model forward and feedback dynamics, effectively decoupling information flow from the underlying interaction structure. Extensive synthetic experiments demonstrate competitive structural recovery performance across diverse graph topologies, including undirected, directed, and weighted graphs. Experiments on COVID-19 data further show that the inferred transmission structures are consistent with real-world population mobility patterns. In addition, the elimination of costly edge-wise computations in DARI leads to substantial gains in both runtime and memory efficiency. Code is available at https://anonymous.4open.science/r/DARI-778C .

This paper presents a novel curvilinear optical proximity correction (OPC) framework. The proposed approach involves representing mask patterns with control points, which are interconnected through cardinal splines. Mask optimization is achieved by iteratively adjusting these control points, guided by lithography simulation. To ensure compliance with mask rule checking (MRC) criteria, we develop comprehensive methods for checking width, space, area, and curvature. Additionally, to match the performance of inverse lithography techniques (ILT), we design algorithms to fit ILT results and resolve MRC violations. Extensive experiments demonstrate the effectiveness of our methodology, highlighting its potential as a viable OPC/ILT alternative.

Model-based optical proximity correction (OPC) with subresolution assist feature (SRAF) generation is a critical standard practice for compensating lithography distortions in the fabrication of integrated circuits at advanced technology nodes. Typical model-based OPC and SRAF algorithms involve the selection of user-controlled rule parameters. Conventionally, these rules are heuristically determined and applied globally throughout the correction regions, which can be time consuming and require expert knowledge of the tool. Additionally, the correlations of rule parameters to the objectives are highly nonlinear. All these factors make designing a high-performance OPC engine for complex metal designs a nontrivial task. This article proposes RuleLearner, a comprehensive mask optimization system designed for SRAF generation and model-based OPC in real industrial scenarios. The proposed framework learns from the guidance of an information-augmented inverse lithography technique engine, which, although expressive for complex designs, is expensive to generate refined masks for a whole set of design clips. Considering the nonlinearity and the tradeoff between local and global performance, the extracted rule value distributions are further optimized with customized natural gradients. The sophisticated SRAF generation, the edge segmentation and movements are then guided by the rule parameter. Experimental results show that RuleLearner can be applied across different complex design patterns and achieve the best lithographic performance and computational efficiency.

Optical proximity correction (OPC) is a vital step to ensure print-ability in modern VLSI manufacturing. Various OPC approaches based on machine learning have been proposed to pursue performance and efficiency, which are typically data-driven and hardly involve any particular considerations of the OPC problem, leading to potential performance or efficiency bottlenecks. In this paper, we propose CAMO, a reinforcement learning-based OPC system that specifically integrates important principles of the OPC problem. CAMO explicitly involves the spatial correlation among the movements of neighboring segments and an OPC-inspired modulation for movement action selection. Experiments are conducted on both via layer patterns and metal layer patterns. The results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry.

Inverse lithography technology (ILT) shows great power in optical proximity correction, which enlarges the solution space of mask optimization and generates high-quality masks in terms of various criteria, including process window. Optimizing the process window involves improving the fidelity of printed wafer patterns on various process conditions. It is non-trivial to explicitly optimize the process window during ILT optimization, which is essentially a multi-objective optimization problem. In this paper, we propose a robust inverse lithography method, RMO-ILT, to optimize the process window effectively. Instead of aggregating all the objectives into a single one, we target the multi-objective optimization directly and explicitly. Specifically, we design a rigorous multi-objective optimization algorithm that computes uniform gradients during the mask optimization process. Furthermore, we improve the algorithm efficiency from both the algorithm level and implementation level to address the intrinsic increase in the computation overhead, significantly reducing the time consumption and enhancing the scalability. Experimental results show that the proposed algorithm achieves superior performance on the process window.

Mask optimization is a vital step in the VLSI manufacturing process in advanced technology nodes. As one of the most representative techniques, optical proximity correction (OPC) is widely applied to enhance printability. Since conventional OPC methods consume prohibitive computational overhead, recent research has applied machine learning techniques for efficient mask optimization. However, existing discriminative learning models rely on a given dataset for supervised training, and generative learning models usually leverage a proxy optimization objective for end-to-end learning, which may limit the feasibility. In this article, we pioneer introducing the reinforcement learning (RL) model for mask optimization, which directly optimizes the preferred objective without leveraging a differentiable proxy. Intensive experiments show that our method outperforms state-of-the-art solutions, including academic approaches and commercial toolkits.

Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. The existing approaches work well when local clients have in-dependent and identically distributed (IID) data but fail to generalize to a more practical FSSL setting, i.e., Non-IID setting. In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by con-sidering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients. Our key motivation is that given models with large deviations from either labeled clients or unlabeled clients, the consensus could be reached by performing random sub-sampling over clients. To achieve it, instead of di-rectly aggregating local models, we first distill several sub-consensus models by random sub-sampling over clients and then aggregating the sub-consensus models to the global model. To enhance the robustness of sub-consensus models, we also develop a novel distance-reweighted model aggre-gation method. Experimental results show that our method outperforms state-of-the-art methods on three benchmarked datasets, including both natural and medical images. The code is available at https://github.com/XMed-Lab/RSCFed.