6 papers
Transfer Learning for Minimum Operating Voltage Prediction in Advanced Technology Nodes: Leveraging Legacy Data and Silicon Odometer Sensing
Yuxuan Yin, Rebecca Chen, Boxun Xu +2
Accurate prediction of chip performance is critical for ensuring energy efficiency and reliability in semiconductor manufacturing. However, developing minimum operating voltage ($V…
Towards the Mitigation of Confirmation Bias in Semi-supervised Learning: a Debiased Training Perspective
Yu Wang, Yuxuan Yin, Peng Li
Semi-supervised learning (SSL) commonly exhibits confirmation bias, where models disproportionately favor certain classes, leading to errors in predicted pseudo labels that accumul…
Data-Efficient Prediction of Minimum Operating Voltage via Inter- and Intra-Wafer Variation Alignment
Yuxuan Yin, Rebecca Chen, Chen He +1
Predicting the minimum operating voltage () of chips stands as a crucial technique in enhancing the speed and reliability of manufacturing testing flow. However, existing…
ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models
Yuxuan Yin, Yu Wang, Boxun Xu +1
Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine l…
Reliable Interval Prediction of Minimum Operating Voltage Based on On-chip Monitors via Conformalized Quantile Regression
Yuxuan Yin, Xiaoxiao Wang, Rebecca Chen +2
Predicting the minimum operating voltage () of chips is one of the important techniques for improving the manufacturing testing flow, as well as ensuring the long-term rel…
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana +5
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Rec…