Training method and test apparatus using the same

A training method comprising:

• for each of first devices to be tested, calculating a first eye width at which a first signal and a second signal synchronize with each other at a first operation speed, and calculating a second eye width at which the first signal and the second signal synchronize with each other at a second operation speed different from the first operation speed;
• performing machine learning on the first eye width and the second eye width to derive a linear regression model showing a relation between operation speeds and the eye widths by using a gradient descent method to calculate parameters of the linear regression model being calculated;
• calculating a third eye width corresponding to a third operation speed different, using the linear regression model;
• finding a third fail-to-pass point, while adjusting the delay of the first signal at the third operation speed;
• and
• determining from the third fail-to-pass point to a point obtained by adding the third eye width to the third fail-to-pass point, as a position of a valid window margin.

A test apparatus comprising:

• output nodes configured to electrically connect to first devices to be tested;
• a timing generator configured to provide a first signal and a second signal to the first devices to be tested through the output nodes;
• and
• a controller configured to control the timing generator,
• wherein the controller is configured to calculate a first eye width at which a first signal and a second signal synchronize with each other at a first operation speed, and calculates a second eye width at which the first signal and the second signal synchronize with each other at a second operation speed different from the first operation speed, for each of first devices to be tested,
• the controller is configured to perform machine learning on the first eye width and the second eye width to derive a model showing a relation between the operation speed and the eye width, and
• the controller is configured to calculate a third eye width corresponding to a third operation speed different from the first operation speed and the second operation speed by using the model.