Minimum Operating Voltage Prediction in Production Test Using Accumulative Learning
Yen-Ting Kuo, Wei-Chen Lin, Chun Chen, Chao-Ho Hsieh, James Chien-Mo Li, Eric Jia-Wei Fang, Sung S.-Y. Hsueh · 2021
We propose a new methodology to predict minimum operating voltage (Vmin) for production chips. In addition, we propose two new key features to improve the prediction accuracy. Our proposed accumulative learning can reduce the impact of lot-to-lot variations. Experimental results on two 7nm industry designs (about 1.2M chips from 142 lots) show that we can achieve above 95% good prediction. Our methodology can save 75% test time compared with traditional testing. To implement this method, we will need to have a separate test flow for the initial training and accumulative training.