Radiation damage in semiconductors is a critical concern for applications in space, nuclear, and high-energy physics environments. The ability to predict and mitigate radiation-induced defects can significantly enhance the reliability and longevity of radiation-hardened (rad-hard) devices. Machine learning (ML) has emerged as a powerful tool for analyzing complex datasets from irradiation experiments, enabling the prediction of defect formation, clustering, and device performance degradation. This article explores the application of machine learning models to these challenges, focusing on neural networks for defect clustering analysis and lifetime prediction in semiconductor materials.

Radiation damage in semiconductors primarily manifests as displacement defects, where high-energy particles knock atoms from their lattice sites, creating vacancies and interstitials. These defects can cluster, forming extended defects that degrade electrical properties, such as carrier lifetime and mobility. Traditional methods for studying radiation damage rely on experimental characterization techniques like deep-level transient spectroscopy (DLTS) and transmission electron microscopy (TEM), combined with physics-based simulations such as molecular dynamics (MD) and density functional theory (DFT). However, these approaches are often computationally expensive and struggle to scale for large datasets. Machine learning offers a complementary approach by identifying patterns in experimental data and accelerating predictions.

A key application of ML in radiation damage studies is defect clustering analysis. Neural networks, particularly convolutional neural networks (CNNs) and graph neural networks (GNNs), have shown promise in classifying and predicting defect configurations. For example, CNNs trained on TEM images of irradiated silicon can automatically identify and categorize defect clusters, such as dislocation loops and voids, with high accuracy. GNNs, which operate on graph-structured data, are particularly effective for modeling interactions between defects in a lattice. By representing the semiconductor crystal as a graph, where nodes are atoms and edges are bonds, GNNs can predict how defects migrate and aggregate under irradiation.

Another critical area is the prediction of device lifetime under radiation exposure. Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are well-suited for time-series data from accelerated irradiation tests. These models can learn the relationship between cumulative radiation dose and performance metrics, such as leakage current or threshold voltage shift, allowing engineers to estimate the operational lifespan of rad-hard devices. For instance, an LSTM model trained on proton irradiation data for silicon carbide (SiC) power devices can forecast degradation trends, aiding in the design of more resilient components.

The effectiveness of ML models depends heavily on the quality and diversity of training data. Datasets from controlled irradiation experiments, such as those conducted at particle accelerators or nuclear reactors, provide essential inputs. Features may include radiation type (e.g., protons, neutrons, gamma rays), energy spectrum, fluence, temperature, and material properties. Supervised learning approaches require labeled data, where defect types and device responses are meticulously recorded. Unsupervised learning methods, such as clustering algorithms, can also uncover hidden patterns in unlabeled datasets, revealing correlations between irradiation conditions and defect formation.

Despite their potential, ML models face challenges in generalizing across different materials and radiation environments. Transfer learning, where a model pre-trained on one material is fine-tuned for another, can mitigate this issue. Additionally, hybrid models that integrate physics-based equations with neural networks—known as physics-informed ML—improve predictive accuracy by enforcing physical constraints. For example, a neural network predicting defect diffusion can incorporate Arrhenius rate equations to ensure temperature-dependent behavior aligns with experimental observations.

The integration of ML into rad-hard device design is still evolving. Current research focuses on optimizing model architectures, improving data collection protocols, and validating predictions against experimental results. As datasets grow and algorithms advance, machine learning will play an increasingly vital role in developing semiconductors capable of withstanding extreme radiation environments. By leveraging these tools, researchers can accelerate the discovery of radiation-tolerant materials and enable the next generation of high-reliability electronics for critical applications.