13 citations · 26 across the 11 of their papers we have counts for
11 papers
AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction
Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang +3
As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacita…
Deep-Learning-Based Pre-Layout Parasitic Capacitance Prediction on SRAM Designs
Shan Shen, Dingcheng Yang, Yuyang Xie +3
To achieve higher system energy efficiency, SRAM in SoCs is often customized. The parasitic effects cause notable discrepancies between pre-layout and post-layout circuit simulatio…
NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation
Haoyuan Li, Dingcheng Yang, Chunyan Pei +1
More accurate capacitance extraction is demanded for designing integrated circuits under advanced process technology. The pattern matching approach and the field solver for capacit…
On the Multi-modal Vulnerability of Diffusion Models
Dingcheng Yang, Yang Bai, Xiaojun Jia +3
Diffusion models have been widely deployed in various image generation tasks, demonstrating an extraordinary connection between image and text modalities. Although prior studies ha…
Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model
Dingcheng Yang, Wenjian Yu, Zihao Xiao +1
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Moreover, the transferability of the adversarial examples has received broad attention in rece…
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge
Dingcheng Yang, Zihao Xiao, Wenjian Yu
Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can…