Semicond. Heterog. Integr., Volume 1, Issue 2 (September 2026) – 2 articles

Semiconductor lithography is a complex cyber-physical system where timely integration of heterogeneous operational data and informed decision-making are critical to take preventive action. Data-driven analytics can predict equipment degradation, but due to poor information pipelines, complex decision logic, and a lack of human expertise integration, it can be difficult to put it into practice. This study aims to produce an integrated framework of industrial informatics for explainable, human-in-the-loop, predictive action in semiconductor lithography using sensor telemetry, maintenance records, and text analytics together with transformer-based approaches and SHAP explainability. The framework is tested with existing industrial lithography data from various facilities, collected in the past under different experimental conditions. Three experiments are conducted to evaluate the predictive performance (LSTM vs. Transformer models), monitoring behavior (anomaly detection using autoencoders), and human–AI collaboration with explainability. Evaluation looks at not only outcomes of human–AI interaction, but also predictive performance and monitoring behavior—not deployment and not causation. Transformer models yield 97.2% accuracy compared to 94.8% for the LSTM baseline (+2.4 pp). Anomaly detection results in a nominal 3.2 pp difference (Autoencoder 94.5% vs. Random Forest 91.3%; $p=0.018$, uncorrected). The integration of human-in-the-loop raises the operator’s trust from 62% to 92% ($p<0.001$) and decision acceptability from 70% to 92%. Under retrospective counterfactual evaluation, operational metrics estimate a 68% reduction in downtime, a 65.1% reduction in yield loss, and a 36% reduction in maintenance costs. Full article

High-index silicon surfaces provide anisotropic step networks, reconstruction states, and facet-adjacent geometries that can modify the first stages of III–V heteroepitaxy. This critical review examines GaAs/AlGaAs growth on Si$\left(hhl\right)$ surfaces, with emphasis on the coupled roles of substrate orientation, surface preparation, first-monolayer initiation, and molecular beam epitaxy kinetics. The central viewpoint is that high-index Si can act as an active interfacial template: its anisotropy can bias early nucleation, relaxation, and faceting pathways before any intentional lithographic patterning is introduced. The discussion is anchored in two recent GaAs/Si studies. The first is a matched-condition benchmark comparing Si(001), Si(113), Si(111), and Si(331) under Ga-first and As-first initiation. The second is a Si(331) case study in which Ga pre-exposure followed by low-rate GaAs nucleation yields laterally ordered nanocorrugation/faceting and measurable in-plane optical anisotropy under the explored conditions. Surface-science precedents from adsorbate-induced reconstructions provide additional context for treating the first atomic layer as a meaningful growth variable. These studies point to a broader opportunity: using high-index Si$\left(hhl\right)$ surfaces to link interface chemistry, anisotropic morphology, structural relaxation, and optical response within a common framework for GaAs/Si integration. Full article