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首页> 外文期刊>IEEE Journal of Solid-State Circuits >An AI-calibrated IF filter: a yield enhancement method with area and power dissipation reductions
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An AI-calibrated IF filter: a yield enhancement method with area and power dissipation reductions

机译:AI校准的IF滤波器:一种提高产量,减少面积和功耗的方法

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摘要

We have developed a large-scale integration (LSI) for Gm-C intermediate frequency (IF) filters, attaining a 63% reduction in filter area, a 26% reduction in power dissipation, compared with existing commercial products using the same process technology and filter topology, and a yield rate of 97%. The developed chip is calibrated within a few seconds by a genetic algorithm - an efficient AI technique for difficult optimization problems. Our calibration method, which can be applied to a wide variety of analog circuits, leads to cost reductions and the efficient implementation of analog LSIs.
机译:与使用相同工艺技术的现有商用产品相比,我们为Gm-C中频(IF)滤波器开发了大规模集成(LSI),与传统的商用产品相比,其滤波器面积减少了63%,功耗降低了26%。滤波器拓扑,良率达97%。遗传算法可在几秒钟内对开发的芯片进行校准,这是一种用于解决优化难题的高效AI技术。我们的校准方法可应用于多种模拟电路,从而可降低成本并有效实施模拟LSI。

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