Intelligence prediction of integrated circuit reliability based on SSA-LSTM fusion architecture

Ying Qian, Bing Liu, Beibei Su and Chunyan Zhang

PLOS ONE, 2025, vol. 20, issue 12, 1-17

Abstract: The relentless scaling of integrated circuits (ICs) into the nanoscale regime has intensified critical reliability challenges, such as Negative Bias Temperature Instability (NBTI), which manifests primarily as a progressive shift in the transistor threshold voltage (ΔVth). This study focuses on the prognostics of digital integrated circuits (ICs) where Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) serve as the fundamental building blocks. Accurate prognostics of device degradation are paramount for predicting circuit lifetime, yet traditional models struggle with the nonlinearity of the degradation process. To address this challenge, a novel fusion architecture is proposed that synergistically combines the Sparrow Search Algorithm (SSA) with a Long Short-Term Memory (LSTM) network. The resulting SSA-LSTM model automates the optimization of crucial LSTM hyperparameters—including the number of hidden units, learning rate, and iteration count—thereby enhancing the capability to learn complex temporal degradation patterns. Empirical Mode Decomposition (EMD) is further integrated as a pre-processing step to denoise the temporal data. This model significantly reduces the average absolute error of ΔVth prediction, outperforming benchmark models such as PSO and EMD-PSO. It can accurately capture the nonlinear trajectory of degradation curves and has high sensitivity for early anomaly detection, providing a high-precision data-driven solution for the reliability evaluation and prediction of digital ICs.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0339394 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 39394&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0339394

DOI: 10.1371/journal.pone.0339394

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().