The paper introduces SchGen, the first large language model capable of generating editable PCB schematics from natural language by using a novel semantically grounded code representation.

The paper introduces Staged Executable Inverse Graphics (SEIG), an agentic framework that uses general-purpose Vision-Language Models (VLMs) to reconstruct editable 3D scenes directly into executable…

The paper proposes using a mask-conditioned latent diffusion model to generate synthetic, labeled TEM images for data augmentation, achieving small but measurable performance improvements in defect de…

The paper introduces SAMSEM, a generalized and scalable model based on SAM2, which significantly improves metal line segmentation across diverse and unseen integrated circuit (IC) samples.

pcbGPT is a grounded system that automatically generates editable KiCad PCB schematics from natural language requirements, achieving high accuracy on complex embedded design tasks.

This review analyzes the dual impact of integrating Large Language Models (LLMs) into hardware design, detailing both their transformative potential in EDA and the critical security vulnerabilities th…

The paper introduces 3DCodeBench, a systematic benchmark and platform for evaluating Vision-Language Model (VLM) agents' ability to generate procedural 3D models from text and images using code.

The paper proposes FLAME, a novel framework that detects AI-generated image forgeries by identifying intrinsic energy anomalies caused by the diffusion process, achieving state-of-the-art localization…

The paper reframes industrial visual sim-to-real transfer as a domain-gap problem categorized by the availability of explicit object geometry (CAD), arguing that the required prior evidence dictates t…

The paper introduces CASS-RTL, a novel, model-agnostic framework that enhances the functional correctness of Large Language Models (LLMs) generating Register-Transfer Level (RTL) code by leveraging th…

The paper proposes a graph attention-based virtual metrology framework that accurately predicts film thickness in semiconductor deposition by modeling structured, directional dependencies among hetero…

This review surveys advanced techniques—including generative models, multimodal learning, and closed-loop workflows—for automated inverse materials design, enabling the targeted discovery of novel cry…

The paper introduces LL-Bench, a comprehensive benchmark for evaluating large-scale generative models on low-level vision tasks, and proposes LL-Score, an MLLM-based evaluator that better aligns quali…

The paper introduces MUSE, a comprehensive benchmark that evaluates Text-to-CAD generation by assessing complex assemblies based on functionality, manufacturability, and assemblability, moving beyond…

RefDiffNet is a lightweight, plug-and-play module that enhances PCB defect detection by comparing the defective image to a defect-free reference image, significantly improving detection accuracy with…

The paper analyzes existing hardware Trojan datasets to demonstrate that standard cell libraries can be systematically exploited to create visually undetectable, stealthy hardware Trojans, exemplified…

This paper identifies the 'Format-Reliability Gap'—where LLMs know about code vulnerabilities but generate insecure code anyway—and proposes a localized, per-vulnerability steering vector fix that sig…

This paper introduces a dual-layer side-channel attack framework that exploits the variable workload introduced by dynamic image preprocessing in local Vision-Language Models (VLMs) to infer sensitive…

The paper demonstrates that off-the-shelf image diffusion models, like Stable Diffusion, can be repurposed to generate synthetic structured data, posing a threat of ground truth drift in closed eviden…