Semiconductor device analysis method, system, and program product thereof

Abstract

A semiconductor device analysis method is provided, comprising: a training data collection step, which acquires at least one training data set, including at least one characteristic parameter, and time series data of the at least one characteristic parameter measured at fixed time intervals in a first time period and a second time period longer than the first time period; a model training step, which trains an artificial intelligence model based on the training data to learn the pattern of the characteristic parameter changing over time; an input data acquisition step, which acquires the time series data of the characteristic parameter measured in the first time period of the new plan when the semiconductor device is used in the new plan, as input data; and a prediction generation step, which inputs the input data into the trained artificial intelligence model, and the artificial intelligence model generates a predicted time series data of the characteristic parameter of the second time period of the new plan.

Claims

1. A semiconductor device analysis method, comprising: a training data collection step, which involves acquiring at least one set of training data, the training data including: The system comprises: a time series data of at least one characteristic parameter of a semiconductor device, measured at fixed time intervals during a first time period and a second time period longer than the first time period; a training model step, training an artificial intelligence model based on the training data to learn the pattern of change of the characteristic parameter over time; an input data acquisition step, acquiring the time series data of the characteristic parameter measured during the first time period of a new project when the semiconductor device is used in a new project, as input data; and a prediction generation step, inputting the input data into the trained artificial intelligence model, and having the trained artificial intelligence model subsequently generate a predicted time series data of the characteristic parameter of the semiconductor device during the second time period of the new project based on the input data.

2. The semiconductor device analysis method as described in claim 1, wherein the artificial intelligence model comprises a generative model.

3. The semiconductor device analysis method as described in claim 1, wherein the first time period corresponds to a high-temperature operating life test period of 168 hours; and the second time period corresponds to a high-temperature operating life test period of 1000 hours.

4. The semiconductor device analysis method as described in claim 3 further includes: a decision step, which compares the predicted time series data with a predetermined reliability standard; and when the predicted time series data meets the predetermined reliability standard, it is determined that the high-temperature operating life test corresponding to the second time period can be omitted from the semiconductor device.

5. The semiconductor device analysis method as described in claim 1, wherein the training data is derived from time-series data of the characteristic parameter collected during previous high-temperature operation life tests of the semiconductor device.

6. The semiconductor device analysis method as described in claim 1, wherein the artificial intelligence model generates sequence data representing the continuous change of the at least one characteristic parameter over time based on the input data.

7. The semiconductor device analysis method as described in claim 1, wherein the semiconductor device is an analog integrated circuit (Analog IC).

8. The semiconductor device analysis method as described in claim 1, wherein the second time period includes a period that overlaps with the first time period.

9. A semiconductor device analysis system, comprising: a memory storing at least one instruction; and a processor coupled to the memory, the processor configured to execute the instructions, causing the system to perform the following operation: a training data collection step, which involves acquiring at least one training data, the training data including: The system comprises: a time series data of at least one characteristic parameter of a semiconductor device, measured at fixed time intervals during a first time period and a second time period longer than the first time period; a training model step, training an artificial intelligence model based on the training data to learn the pattern of change of the characteristic parameter over time; an input data acquisition step, acquiring the time series data of the characteristic parameter measured during the first time period of a new project when the semiconductor device is used in a new project, as input data; and a prediction generation step, inputting the input data into the trained artificial intelligence model, and having the trained artificial intelligence model subsequently generate a predicted time series data of the characteristic parameter of the semiconductor device during the second time period of the new project based on the input data.

10. A semiconductor device analysis program product for enabling a computer to perform the following operation: a training data collection step, which involves acquiring at least one training data set, the training data including: The system comprises: a time series data of at least one characteristic parameter of a semiconductor device, measured at fixed time intervals during a first time period and a second time period longer than the first time period; a training model step, training an artificial intelligence model based on the training data to learn the pattern of change of the characteristic parameter over time; an input data acquisition step, acquiring the time series data of the characteristic parameter measured during the first time period of a new project when the semiconductor device is used in a new project, as input data; and a prediction generation step, inputting the input data into the trained artificial intelligence model, and having the trained artificial intelligence model subsequently generate a predicted time series data of the characteristic parameter of the semiconductor device during the second time period of the new project based on the input data.

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