What is predictive quality in manufacturing?

Predictive quality uses production and quality data, analysed with statistical and machine learning models, to forecast whether a product will meet its quality requirements. Teams can then correct the process before defects occur instead of finding them at the end of the line.

Why do machine learning models sometimes fail to predict product quality?

Pure ML models learn only from the data they receive. When many factors are unmonitored, or when product variants behave differently, the model sees noise it cannot explain and its accuracy drops. Making the model more complex is often costly and inefficient.

What is physics-informed machine learning?

Physics-informed machine learning combines equations that describe the physical behaviour of a process with data-driven models. The physics provides a stable, interpretable baseline, and machine learning captures the effects the equations do not cover.

How is AI used in semiconductor manufacturing?

Common applications include predictive quality, predictive maintenance of equipment, anomaly detection in process data and process parameter optimisation. Results are most reliable when AI models are combined with the process knowledge of engineers.

What data do you need for predictive quality?

At minimum, process parameters from machines and PLCs and quality results from inspection or quality control stations, linked to the same product or batch. Context from production systems such as MES improves the model further.

Semiconductor Quality Control with Physics-Informed ML

Semiconductor quality control improves when prediction models respect the physics of the process. In this project, a semiconductor manufacturer connected its machines and quality control stations to predict product quality, but its first machine learning (ML) model was not accurate enough. Rebuilding the model on physical equations, with ML working on top of them, made the predictions reliable and helped the team react to quality problems faster.

Below we explain the challenge, what went wrong, how TT PSC corrected the approach and what other manufacturers can take from it when they add AI to their digital manufacturing programmes.

Goal: predict the quality of final products from process and quality data.
Data sources: PLCs, production support systems and quality control stations.
Problem: an AI model based only on process parameters had low accuracy.
Fix: a new model based on physical equations, with the AI/ML algorithm working on this foundation.
Result: more accurate, consistent quality predictions and faster reaction to potential problems.

The challenge: quality control in semiconductor manufacturing

A company in the electronics industry, specialising in semiconductor manufacturing, set itself a major goal: to improve the quality of its final products. The key was to monitor production and identify the correlations that predict whether a product coming off the line will meet quality requirements.

The raw material for this work was data from machines and quality control stations. Subjected to in-depth analysis, it would show the company which factors actually affect the quality of its products.

Why inspection alone is not enough

Semiconductor production runs through long chains of tightly controlled process steps, and several product variants often share the same equipment. Conventional quality control, based on inspection, metrology and statistical process control (SPC), confirms whether a product or parameter is within limits. It does not reveal early enough which combination of process conditions will lead to a defect. Predictive quality analytics closes that gap by using process data to forecast the outcome before final testing.

The solution: connected production data and predictive quality

Working with TT PSC, the company implemented a solution that included integration with its PLCs and production support systems. Integration comes first in any predictive quality project, because a model can only be as good as the process and quality data it can see.

The core of the project was historical data analysis and modelling with predictive quality capabilities. The aim was to predict potential quality problems, catch them quickly, solve them and prevent them from happening again.

The data foundation behind predictive quality

This is the same foundation that supports any connected factory. Machine data is collected from PLCs through industrial connectivity and combined with quality results and production context, which in most plants comes from a manufacturing execution system (MES) or a quality management system. In an Industrial IoT (IIoT) architecture, this data is available to analytics and AI models without manual exports.

Why the first AI model failed

Despite advanced artificial intelligence (AI) modelling based on process parameters, the first version of the solution proved ineffective and its accuracy was low.

The team traced the problem to two causes. There were too many factors in play, both monitored and unmonitored, and the company produced various product variants. Covering all of this with more complex models would have been costly and inefficient. As a result, the algorithms could not accurately predict product quality after the manufacturing process.

This is a typical limit of purely data-driven models. They learn the correlations present in the data they receive, so unmonitored factors and differences between product series appear as noise that the model cannot explain.

The fix: physics-based modelling for semiconductor quality control

In response, TT PSC experts built a new model based on physical equations. Instead of relying solely on AI and ML algorithms to analyse process data, they grounded the approach in fundamental physics principles. The AI/ML algorithm then worked on this physics-based foundation, an approach often described as hybrid or physics-informed modelling.

How physics-based models improve quality predictions

Lower complexity: models based on physical equations can be less complex than advanced AI models, because they rely on well-understood physics rather than on analysing large amounts of data. This reduces the risk of errors caused by an excessive number of variables.
Easier interpretation: physical models are often more transparent than AI models, so engineers can better understand which factors affect product quality.
Stability and consistency: physical models rest on fixed laws of physics rather than on process data that can vary from one product series to another.
Scalability: physical models are easier to scale and adapt to different product variants, which is harder to achieve with complex AI models.

Pure machine learning vs physics-informed modelling

Criterion	Pure ML model on process parameters	Physics-informed (hybrid) model
Basis	Correlations in historical process data	Physical equations, with ML working on top
Complexity	Grows with every new factor and product variant	Lower, anchored in well-understood physics
Interpretability	Often hard for engineers to explain	Transparent, factors can be traced
Stability	Sensitive to differences between product series	Stable, based on fixed physical laws
New product variants	Costly, often requires larger models	Easier to scale and adapt
Outcome in this project	Low accuracy	Accurate, consistent predictions

Results: reliable predictions and faster response

The physics-based approach delivered more accurate product quality predictions. It removed the influence of excessive variables and differences between product series, so the results became more consistent and reliable.

The sequence matters. On its own, the ML model used to analyse process parameters could not reach the expected accuracy because of the number of factors and the differences between product series. Only after standard physics-based modelling equations were introduced did the AI/ML algorithm work effectively. As a result, the company can control the quality of its products better and react faster to potential problems.

Four lessons for AI in semiconductor manufacturing

The project leads to several conclusions that apply well beyond the semiconductor industry:

Even advanced algorithms should produce results that are easy to interpret and practical to use.
Algorithms should support decision-making by providing valuable information.
Industry expertise and the experience of manufacturing technologists are key to implementing machine learning successfully.
Algorithms can analyse data that the intuitive approach of experts would typically overlook.

The strongest results come from combining the process knowledge of engineers with the pattern-finding ability of AI, not from choosing one over the other. Our article on predictive AI vs generative AI in manufacturing explains how to match each type of model to the decision it supports.

How to apply physics-informed predictive quality in your plant

Connect the data sources. Integrate PLCs, quality control stations and production systems so that process and quality data can be analysed together.
Start from the physics. Ask process engineers and technologists which physical relationships govern the quality characteristic you want to predict.
Build a physics-based baseline. Use it to describe the known behaviour of the process.
Add machine learning where the equations stop. Let ML capture the effects that the physical model does not describe.
Validate across product variants. Check that predictions stay accurate when the product mix changes.
Put predictions into daily work. Show them in dashboards and reports so teams can act before a defect reaches final testing.

Where predictive quality fits in digital manufacturing

Predictive quality is one of several use cases that turn connected production data into better decisions. In a smart factory, it works alongside predictive maintenance, which applies the same data foundation to equipment health, and production monitoring with OEE. Together, these use cases form the analytics layer of manufacturing operations management (MOM), fed by PLC data, MES records and quality results.

Physics-based process models are also a natural building block for a digital twin, because they describe how a process should behave, not only how it behaved in the past. Reliable quality forecasts help planning and supply chain teams commit to delivery dates with more confidence. For a wider view of how these pieces connect, see our Industry 4.0 use cases and Data and AI services.

Summary: what this case teaches about semiconductor quality control

Semiconductor quality control becomes predictive when process and quality data are connected and analysed together. This project showed that data alone is not enough. A pure ML model struggled with too many variables and product variants, while a model built on physical equations, with ML working on top, delivered accurate and consistent predictions. For manufacturers planning AI for manufacturing, the lesson is to combine the knowledge of technologists with data and to make sure every prediction is one that engineers can understand and act on.

Frequently asked questions

Semiconductor quality control is the set of methods used to make sure chips and components meet their specifications, from inline inspection and metrology to statistical process control and final electrical testing. Predictive quality adds a forward-looking layer by using process data to forecast quality before the final test.

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Semiconductor Quality Control: How Physics-Based Models Made AI Predictions Reliable