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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.00516 |
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| _version_ | 1866929542487080960 |
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| author | Cao, Jiawei Guo, Chongtao Li, Hao Wang, Zhigang Wang, Houjun Li, Geoffrey Ye |
| author_facet | Cao, Jiawei Guo, Chongtao Li, Hao Wang, Zhigang Wang, Houjun Li, Geoffrey Ye |
| contents | In this paper, we propose a deep learning based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0-1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00516 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning based Performance Testing for Analog Integrated Circuits Cao, Jiawei Guo, Chongtao Li, Hao Wang, Zhigang Wang, Houjun Li, Geoffrey Ye Systems and Control In this paper, we propose a deep learning based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0-1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost. |
| title | Deep Learning based Performance Testing for Analog Integrated Circuits |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2406.00516 |