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Minimum Operating Voltage Prediction in Production Test Using Accumulative Learning
We propose a new methodology to predict minimum operating voltage (V min ) 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 industr...
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| Format: | Conference Proceeding |
| Language: | English |
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| container_end_page | 52 |
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| creator | Kuo, Yen-Ting Lin, Wei-Chen Chen, Chun Hsieh, Chao-Ho Li, James Chien-Mo Jia-Wei Fang, Eric Hsueh, Sung S.-Y. |
| description | We propose a new methodology to predict minimum operating voltage (V min ) 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. |
| doi_str_mv | 10.1109/ITC50571.2021.00012 |
| format | conference_proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2378-2250 |
| ispartof | 2021 IEEE International Test Conference (ITC), 2021, p.47-52 |
| issn | 2378-2250 |
| language | eng |
| recordid | cdi_ieee_primary_9611347 |
| source | IEEE Xplore All Conference Series |
| subjects | Chip Performance Prediction Conferences Industries Machine Learning Process variation Ring oscillators Semiconductor device modeling Training Training data Voltage |
| title | Minimum Operating Voltage Prediction in Production Test Using Accumulative Learning |
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