Fault Detection and Diagnosis in Semiconductor Manufacturing Systems

Fault detection and diagnosis in semiconductor fabrication seek to identify, classify and locate anomalies in highly complex, multi‐step production processes before they compromise device yield and performance. Modern fabs deploy extensive sensor networks to capture temperature, pressure, gas flow, plasma characteristics and other critical parameters in real time. Traditional statistical process control charts have evolved into sophisticated data‐driven frameworks that combine feature extraction, multivariate monitoring and machine‐learning models. These methods enable early anomaly detection, root‐cause inference and predictive maintenance, thereby minimising unplanned downtime, reducing scrap and optimising throughput. Advances in unsupervised and supervised learning, deep neural architectures and causal analysis have enhanced the capability to diagnose subtle equipment drifts, tool misalignments and process shifts. Integration with Industry 4.0 platforms and cloud‐based analytics now supports automated recovery actions and closed‐loop control. As wafer geometries shrink and process windows narrow, robust fault diagnosis becomes ever more critical to sustain global chip supply chains and meet the increasing demands of advanced logic and memory devices.

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Fault Detection and Diagnosis in Semiconductor Manufacturing Systems publication trend

The graph below shows the total number of articles in fault detection and diagnosis in semiconductor manufacturing systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fault Detection and Diagnosis (FDD): The combined process of identifying anomalies in equipment or process data and determining their underlying causes to support corrective actions.

Predictive Maintenance: A maintenance strategy that forecasts equipment failures based on data analysis, enabling timely interventions before breakdowns occur.

Artificial Immune System (AIS): A computational algorithm inspired by the human immune response, used for anomaly detection and classification in complex systems.

Generative Adversarial Network (GAN): A deep‐learning framework comprising two competing neural networks—generator and discriminator—used to synthesize realistic data samples.

Residual Network (ResNet): A neural network architecture featuring skip connections that ease the training of very deep models by mitigating gradient vanishing.

Optical Emission Spectroscopy (OES): A diagnostic technique that analyses light emitted by plasma discharges to infer process conditions and detect faults.

References

1. Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’. Applied Sciences (2023).
2. Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference. IEEE Access (2021).
3. Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment. Electronics (2021).
4. Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data. Sensors (2023).
5. Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment †. Sensors (2023).