Method and system for predicting service life of MOS (Metal Oxide Semiconductor) device and electronic equipment

By constructing a continuous time dynamic model and multi-time scale decomposition technology, combining adaptive numerical solution and physical constraint regularization, the multi-scale characteristic processing and synergistic effect problems in MOS device life prediction are solved, and high-precision device life prediction is achieved.

CN120257843AInactive Publication Date: 2025-07-04深圳市和芯电子有限公司
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Patent Information

Application Number
CN202510724652.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-03
Publication Date
2025-07-04
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the prior art, the MOS device lifetime prediction method has problems such as insufficient discrete time modeling, neglecting synergies between devices, difficulty in processing multi-time scale characteristics, and insufficient physical interpretability, resulting in insufficient prediction accuracy and reliability.

Method used

The device relationship diagram is constructed using a continuous time dynamic model, combining multi-time scale decomposition and adaptive numerical solution algorithm, and precise modeling and prediction of the MOS device degradation process through graph attention mechanism and physical constraint regularization.

Benefits of technology

It improves the accuracy and reliability of MOS device life prediction, can handle irregular sampled data, enhances the physical rationality and generalization capabilities of the model, and is suitable for integrated circuit systems with high integration and high reliability requirements.

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Abstract

The invention relates to the technical field of integrated circuit reliability, and discloses an MOS device service life prediction method and system and electronic equipment, and the method comprises the steps: constructing a continuous time dynamic model, and expressing the degradation process of an MOS device; constructing a device relation graph structure, and capturing spatial correlation between devices; executing multi-time scale decomposition, and analyzing time sequence characteristics of device degradation; a self-adaptive numerical solution algorithm is applied to realize degradation track prediction; physical constraint regularization is implemented, and the physical rationality of the model is improved. By fusing the graph neural differential equation and the multi-time scale decomposition technology, the problems of insufficient discrete time modeling, neglect of a synergistic effect between devices, difficulty in multi-scale characteristic processing and the like in the prior art are solved, and high-precision prediction of the service life of the MOS device is realized.
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Citation Information

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