3 citations · 5 across the 8 of their papers we have counts for
8 papers
PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images
Zhen Huang, Yuhao Gao, Yuzhi Liu +15
Printed circuit boards (PCBs) are fundamental to modern electronic systems, yet AI-driven PCB design automation remains constrained by the lack of large-scale paired schematic-netl…
AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits
Ze Zhang, Junzhuo Zhou, Yichen Shi +6
Analog and mixed-signal (AMS) circuit design remains heavily reliant on expert knowledge. While recent AI-driven automation tools can generate candidate topologies, they critically…
Automated SAR ADC Sizing Using Analytical Equations
Zhongyi Li, Zhuofu Tao, Yanze Zhou +4
Conventional analog and mixed-signal (AMS) circuit designs heavily rely on manual effort, which is time-consuming and labor-intensive. This paper presents a fully automated design…
AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection
Yichen Shi, Zhuofu Tao, Yuhao Gao +5
Current multimodal large language models (MLLMs) struggle to understand circuit schematics due to their limited recognition capabilities. This could be attributed to the lack of hi…
AMSbench: A Comprehensive Benchmark for Evaluating MLLM Capabilities in AMS Circuits
Yichen Shi, Ze Zhang, Hongyang Wang +10
Analog/Mixed-Signal (AMS) circuits play a critical role in the integrated circuit (IC) industry. However, automating Analog/Mixed-Signal (AMS) circuit design has remained a longsta…
AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG
Yichen Shi, Zhuofu Tao, Yuhao Gao +9
High-performance analog and mixed-signal (AMS) circuits are mainly full-custom designed, which is time-consuming and labor-intensive. A significant portion of the effort is experie…
AMSNet: Netlist Dataset for AMS Circuits
Zhuofu Tao, Yichen Shi, Yiru Huo +8
Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled signif…
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
Yichen Shi, Yuhao Gao, Yingxin Lai +7
Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks, capitalizing on their visual semantic comprehension and reasoning capabiliti…