Qihang Wang

Lik Tung Fu, Qihang Wang, Shaokai Ren et al.

Functional verification remains a dominant cost in modern IC development, and SystemVerilog Assertions (SVAs) are critical for simulation-based monitoring and formal property checking. However, writing SVAs by hand is time-consuming and error-prone. Directly prompting general-purpose large language models (LLMs) is also unreliable: the generated properties are often syntactically invalid or semantically incorrect, and the problem is exacerbated by scarce, high-quality domain training data. We present SVA Generator, a data-centric framework that translates natural-language SVA Descriptions (SVADs) into executable SVAs. It uses AST-grounded constraint injection and an automated supervision pipeline that enforces structural consistency and reduces hallucinations via de-duplication and constraint checks. To enable rigorous evaluation, we introduce a benchmark suite stratified by AST depth and use formal property equivalence checking to quantify semantic correctness separately from syntax validity, by checking mutual implication between the generated and reference properties under the same clocking and environment assumptions. Across all difficulty tiers, SVA Generator achieves comparable Syntax Pass Rate (SPR) to strong general LLM baselines, while delivering substantially higher Semantic Equivalence Rate (SER) on deeper tiers: +24.5 pp on D2, +26.0 pp on D3, and +17.5 pp on D4 relative to the best-performing general LLM, corresponding to a +22.7 pp SER improvement on average over D2--D4. These results highlight that high-fidelity data construction and depth-stratified benchmarking are key to reliable, semantics-preserving SVA generation.

Haohang Xu, Chengjie Liu, Qihang Wang et al.

Large Language Model (LLM) exhibits great potential in designing of analog integrated circuits (IC) because of its excellence in abstraction and generalization for knowledge. However, further development of LLM-based analog ICs heavily relies on textual description of analog ICs, while existing analog ICs are mostly illustrated in image-based circuit diagrams rather than text-based netlists. Converting circuit diagrams to netlists help LLMs to enrich the knowledge of analog IC. Nevertheless, previously proposed conversion frameworks face challenges in further application because of limited support of image styles and circuit elements. Up to now, it still remains a challenging task to effectively convert complex circuit diagrams into netlists. To this end, this paper constructs and opensources a new dataset with rich styles of circuit diagrams as well as balanced distribution of simple and complex analog ICs. And a hybrid framework, named Image2Net, is proposed for practical conversion from circuit diagrams to netlists. The netlist edit distance (NED) is also introduced to precisely assess the difference between the converted netlists and ground truth. Based on our benchmark, Image2Net achieves 80.77\% successful rate, which is 34.62\%-45.19\% higher than previous works. Specifically, the proposed work shows 0.116 averaged NED, which is 62.1\%-69.6\% lower than state-of-the-arts.

Qihang Wang, Yaxiong Wang, Lechao Cheng et al.

This paper explores image editing under the joint control of text and drag interactions. While recent advances in text-driven and drag-driven editing have achieved remarkable progress, they suffer from complementary limitations: text-driven methods excel in texture manipulation but lack precise spatial control, whereas drag-driven approaches primarily modify shape and structure without fine-grained texture guidance. To address these limitations, we propose a unified diffusion-based framework for joint drag-text image editing, integrating the strengths of both paradigms. Our framework introduces two key innovations: (1) Point-Cloud Deterministic Drag, which enhances latent-space layout control through 3D feature mapping, and (2) Drag-Text Guided Denoising, dynamically balancing the influence of drag and text conditions during denoising. Notably, our model supports flexible editing modes - operating with text-only, drag-only, or combined conditions - while maintaining strong performance in each setting. Extensive quantitative and qualitative experiments demonstrate that our method not only achieves high-fidelity joint editing but also matches or surpasses the performance of specialized text-only or drag-only approaches, establishing a versatile and generalizable solution for controllable image manipulation. Code will be made publicly available to reproduce all results presented in this work.