Method for modeling serializer/deserializer model and method for manufacturing serializer/deserializer
AssigneeSamsung Electronics Co., Ltd.
InventorsWook KIM, Soonwon KWON, Inhwan SONG, Beomkyu SEO, Donghyuk LIM
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Abstract
A method for modeling a serializer/deserializer (SerDes) model includes generating plural data sets including noise simulation data of the SerDes model and output measurement data of an actual SerDes, training a machine learning model based on the plural data sets, and applying the trained machine learning model and an estimation model to a model included in the SerDes model. The estimation model provides the noise simulation data as an input to the trained machine learning model.
Claims20
A method implemented on a computer for modeling a serializer/deserializer (SerDes) model, the method comprising:
- generating a plurality of data sets comprising noise simulation data of the SerDes model and output measurement data of an actual SerDes;
- training a machine learning model based on the plurality of data sets;
- and
- applying the trained machine learning model and an estimation model to a model included in the SerDes model, the estimation model being configured to provide the noise simulation data as an input to the trained machine learning model.
A method for manufacturing a serializer/deserializer (SerDes), the method comprising:
- modeling a SerDes model comprising a transmission model, a channel model, and a reception model on a computer;
- and
- manufacturing a SerDes chip corresponding to the SerDes model,
- wherein the modeling of the SerDes model comprises:
- generating a plurality of data sets comprising noise simulation data of the SerDes model and output measurement data of an actual SerDes;
- training a machine learning model based on the plurality of data sets;
- and
- applying the trained machine learning model and an estimation model to one of the transmission model, the channel model, and the reception model, the estimation model being configured to provide the noise simulation data as an input to the trained machine learning model.
A computer-readable recording medium storing a computer program which, when executed by a computer, causes the computer to:
- generate a plurality of data sets comprising noise simulation data of a serializer/deserializer (SerDes) model, and output measurement data of an actual SerDes;
- train a machine learning model based on the plurality of data sets;
- and
- apply the trained machine learning model and an estimation model to a model included in the SerDes model, the estimation model being configured to provide the noise simulation data as an input to the trained machine learning model.
Dependencies are inferred from the claim text.