Accurate and efficient 3D photoresist simulation is essential for optical lithography at advanced technology nodes. Existing methods that predict 3D resist profiles from aerial images either rely on analytical reaction-diffusion solvers, which are slow, or on high-capacity 3D generative models, which are costly to train and deploy. We instead formulate 3D resist prediction as a depth-wise 2D generation task conditioned on the aerial image. DiffResist introduces a physics-constrained diffusion model whose reverse steps are aligned with resist exposure physics: a two-stage noise schedule connects physically meaningful layers to a Gaussian prior, and boundary conditions at the resist-air interface are injected to suppress error propagation. Combined with a lightweight super-resolution module, DiffResist achieves state-of-the-art accuracy on a public benchmark with over 10 × faster inference than 3D diffusion baselines.

Recently, printed circuit board (PCB) placement has emerged as a significant challenge since the scale of PCB designs has rapidly enlarged. Furthermore, the presence of various types of constraints with differing tolerance priorities hampers the automation of PCB layout design, necessitating substantial manual effort. To address this problem, we introduce a novel agent-based framework that automatically generates PCB layouts meeting industrial constraints through user interactions. This framework includes two main agents: a reinforcement learning (RL)-based agent for layout inference and fine-tuning, and a large language model (LLM)-based agent for interactive optimization. Experimental results on 17 industrial tasks show that our framework outperforms other state-of-the-art methods.

The microarchitecture design of processors faces growing complexity due to expanding design space and time-intensive verification processes. Utilizing historical design task data can improve the search process, but managing distribution discrepancies between different source tasks is essential for enhancing the search method's generalization ability. In light of this, we introduce IT-DSE, a microarchitecture searching framework with the surrogate model pre-trained to absorb knowledge from previous design tasks. The Feature Tokenizer-Transformer (FT-Transformer) serves as a backbone, facilitating feature extraction from source tasks even with varied design spaces. Concurrently, the invariant risk minimization (IRM) paradigm bolsters generalization ability under data distribution discrepancies. Further, IT-DSE exploits a combination of multi-objective Bayesian optimization and a model ensemble to discover Pareto-optimal designs Experimental results indicate that IT-DSE effectively harnesses the knowledge of existing microarchitecture designs and uncovers designs that outperform previous methods in terms of power, performance, and area (PPA).

The decrease of feature size and the growing complexity of the fabrication process lead to more failures in manufacturing semiconductor devices. Therefore, identifying the root cause layout patterns of failures becomes increasingly crucial for yield improvement. In this article, a novel layout-aware diagnosis-based layout pattern analysis framework is proposed to identify the root cause efficiently. At the first stage of the framework, an encoder network trained using contrastive learning is used to extract representations of layout snippets that are invariant to trivial transformations, including shift, rotation, and mirroring, which are then clustered to form layout patterns. At the second stage, we model the causal relationship between any potential root cause layout patterns and the systematic defects by a structural causal model, which is then used to estimate the average causal effect (ACE) of candidate layout patterns on the systematic defect to identify the true root cause. Experimental results on real industrial cases demonstrate that our framework outperforms a commercial tool with higher accuracies and around$\times 8.4$speedup on average.

Identifying root causes of systematic defects is a crucial step in yield enhancement process of integrated circuit (IC) manufacturing. With increasing complexity of fabrication processes and decreasing sizes of pattern features, more systematic defects occur at advanced technology nodes, and traditional methods are unfeasible to directly identify failure causes, due to expensive time and labor costs. Root cause analysis (RCA) technology is thus studied to automatically identify common root causes in a short time. In this paper, we develop RCANet, an end-to-end unsupervised learning-based RCA framework, which analyses diagnosis reports of failing dies within a wafer and identifies both layout-aware and cell-internal root causes efficiently. Experimental results on designs with different technologies demonstrate that RCANet outperforms both a commercial tool and the state-of-the-art method.

In a nonparametric setting, the causal structure is often identifiable only up to Markov equivalence, and for the purpose of causal inference, it is useful to learn a graphical representation of the Markov equivalence class (MEC). In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorithm for learning the MEC of the underlying causal structure. We observe that in order to make the GES algorithm consistent in a nonparametric setting, it is not necessary to design a scoring metric that evaluates graphs. Instead, it suffices to plug in a consistent estimator of a measure of conditional dependence to guide the search. We therefore present a reframing of the GES algorithm, which is more flexible than the standard score-based version and readily lends itself to the nonparametric setting with a general measure of conditional dependence. In addition, we propose a neural conditional dependence (NCD) measure, which utilizes the expressive power of deep neural networks to characterize conditional independence in a nonparametric manner. We establish the optimality of the reframed GES algorithm under standard assumptions and the consistency of using our NCD estimator to decide conditional independence. Together these results justify the proposed approach. Experimental results demonstrate the effectiveness of our method in causal discovery, as well as the advantages of using our NCD measure over kernel-based measures.

The decrease of feature size and the growing complexity of the fabrication process lead to more failures in manufacturing semiconductor devices. Therefore, identifying the root cause layout patterns of failures becomes increasingly crucial for yield improvement. In this paper, a novel layout-aware diagnosis-based layout pattern analysis framework is proposed to identify the root cause efficiently. At the first stage of the framework, an encoder network trained using contrastive learning is used to extract representations of layout snippets that are invariant to trivial transformations including shift, rotation, and mirroring, which are then clustered to form layout patterns. At the second stage, we model the causal relationship between any potential root cause layout patterns and the systematic defects by a structural causal model, which is then used to estimate the Average Causal Effect (ACE) of candidate layout patterns on the systematic defect to identify the true root cause. Experimental results on real industrial cases demonstrate that our framework outperforms a commercial tool with higher accuracies and around x8.4 speedup on average.

The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model. In practice, however, observations are often collected from multiple sources with heterogeneous causal models due to certain uncontrollable factors, which renders causal analysis results obtained by a single model skeptical. In this paper, we generalize the Additive Noise Model (ANM) to a mixture model, which consists of a finite number of ANMs, and provide the condition of its causal identifiability. To conduct model estimation, we propose Gaussian Process Partially Observable Model (GPPOM), and incorporate independence enforcement into it to learn latent parameter associated with each observation. Causal inference and clustering according to the underlying generating mechanisms of the mixture model are addressed in this work. Experiments on synthetic and real data demonstrate the effectiveness of our proposed approach.

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods.