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Generating Routing-Driven Power Distribution Networks With Machine-Learning Technique.

, , , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 36 (8): 1237-1250 (2017)

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Mask versus Schematic - an enhanced design-verification flow for first silicon success., , , , and . ITC, page 369-377. IEEE Computer Society, (2010)An Emergency System in Mobile Devices., and . PAKDD Workshops, volume 8643 of Lecture Notes in Computer Science, page 194-198. Springer, (2014)Power Distribution Network Generation for Optimizing IR-Drop Aware Timing., , , and . ICCAD, page 146:1-146:9. IEEE, (2020)SpeedER: A Supervised Encoder-Decoder Driven Engine for Effective Resistance Estimation of Power Delivery Networks., , , and . MLCAD, page 55-61. ACM / IEEE, (2022)Generating Routing-Driven Power Distribution Networks with Machine-Learning Technique., , , , , , and . ISPD, page 145-152. ACM, (2016)Generating Routing-Driven Power Distribution Networks With Machine-Learning Technique., , , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 36 (8): 1237-1250 (2017)Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques., , , , , , , and . ASP-DAC, page 426-431. IEEE, (2017)Statistical methodology to identify optimal placement of on-chip process monitors for predicting fmax., , , , , and . ICCAD, page 116. ACM, (2016)Testing retention flip-flops in power-gated designs., , , , , and . VTS, page 1-6. IEEE Computer Society, (2013)