Solved inspection challenges for a semiconductor manufacturer

Our solution

Our team of AI developers worked closely with the client to understand their inspection workflow and identify root causes of recurring issues.

We proposed an AI-driven solution integrating machine learning, deep learning, and computer vision to enhance defect detection accuracy, automate the inspection process, and significantly reduce reliance on manual reviews.

Data collection and preprocessing from chip manufacturing processes

In this first phase, we gathered and preprocessed various types of data from semiconductor manufacturing processes systematically.

• Types of data collected: The data includes defect logs, production parameters, equipment performance metrics, environmental situations, and maintenance history.
• Data sources and preprocessing: Here, we collected data from diverse sources including sensors, production databases, and historical maintenance logs.

Application of AI and deep learning techniques for predictive analytics

In this phase, we outlined the specific AI techniques and their basis for use in predictive analytics within semiconductor manufacturing.

• Overview of algorithms: We utilized a variety of machine learning and deep learning algorithms such as decision trees, random forests, support vector machines, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
• Purpose of using AI techniques: Our team of AI experts utilized machine learning as its algorithms had the capability to handle large, complex datasets and find complex patterns. Deep learning models like CNNs and RNNs perform remarkably where image recognition was required to detect defects, and sequential data analysis was imperative for predictive maintenance.

Predictive model development and evaluation using deep learning frameworks

Then, we developed predictive models and trained, validated, and tested them.

• Model development process: We used frameworks like TensorFlow and PyTorch to build the models. We began by defining model architectures, followed by selecting appropriate loss functions, and finally used optimization algorithms to train them.
• Training, validation, and testing: We split data into three sets: training, validation, and testing to ensure the models generalize well. Then, we employed cross-validation techniques to fine-tune hyperparameters and avoid overfitting.

AI model implementation and integration into workflows

In the final phase, we focused on the practical steps required to integrate AI models into semiconductor manufacturing workflows.

• Integration steps: We deployed AI models within existing manufacturing systems. This included real-time data ingestion, model inference, and integration with control systems for automated decision making.
• Tools and platforms: We used tools like Python for scripting, and cloud-based platforms for scalable deployment.