Design Document:Stream Processing Service

Stream Processing Service

• Technology: Apache Kafka, Apache Flink
• Purpose: Real-time processing of equipment sensor data and alerts
• Interfaces:
  • MQTT brokers for IoT sensor data
  • Equipment-specific APIs (Applied Materials, KLA, AMAT)
  • Message queues for high-frequency data streams
• Processing: Real-time anomaly detection, data aggregation, and event correlation

Stream Processing Service: Design and Implementation

Here's a detailed, hands-on design for a real-time stream processing service using Apache Kafka and Apache Flink, focusing on the specific needs of semiconductor manufacturing.

1. High-Level Architecture

The system will use Apache Kafka as a highly scalable message bus and Apache Flink as the stateful stream processing engine. This combination ensures high throughput, low latency, and fault-tolerant processing of continuous data streams from manufacturing equipment.

2. Data Ingestion (Apache Kafka)

Service: Kafka Connect for Data Sourcing

• Objective: To reliably ingest high-frequency sensor data from various equipment and APIs into Kafka topics.
• Technology: Kafka Connect with specialized connectors.
• Implementation Details:
  • MQTT Connector: Use a Kafka Connect MQTT Source Connector to subscribe to MQTT topics from IoT sensors and equipment (e.g., in a clean room). The connector will ingest these messages and publish them to a designated Kafka topic, such as equipment-sensor-data. This is ideal for lightweight, high-volume data.
  • Proprietary API Connectors: For equipment with proprietary APIs (e.g., Applied Materials, KLA), develop custom Kafka Connect Source Connectors. These connectors will be Python or Java services that poll the equipment APIs and write the data to Kafka topics.
  • Topic Design: Each data source will have its own Kafka topic. For example:
    • equipment-sensor-data: for raw sensor readings (temperature, pressure, power).
    • process-events: for events like 'process start', 'recipe change', 'lot move'.
  • Partitioning: Partition Kafka topics by wafer_id or lot_id. This is critical for stateful processing in Flink, as it ensures all related events for a specific wafer or lot are routed to the same Flink task, allowing for accurate aggregations and event correlations.

3. Stream Processing (Apache Flink)

Service: Flink Jobs for Real-time Analytics

• Objective: To consume raw data from Kafka, process it in real time, and derive actionable insights and alerts.
• Technology: Apache Flink with its stateful stream processing capabilities.
• Implementation Details:
  • Real-time Anomaly Detection:
    • Simple Thresholds: Use Flink's basic operators to filter out sensor readings that exceed predefined static or dynamic thresholds. For instance, if a chamber's temperature reading goes above 300°C, an alert is triggered.
    • Statistical Models: For more advanced anomaly detection, implement a Flink job that uses a KeyedProcessFunction. This function can maintain a sliding window of recent sensor readings for each piece of equipment. Using this window, it can calculate a moving average and standard deviation to identify outliers in real time.
    • Machine Learning: For sophisticated, non-linear patterns, use an online learning model. Flink can be integrated with machine learning libraries to train a model (e.g., Isolation Forest) on a continuous stream of data. The model then scores each new incoming data point for its likelihood of being an anomaly.
  • Data Aggregation:
    • Windowing: Use Flink's Tumbling Windows or Sliding Windows to aggregate data over specific time periods. For example, a tumbling window of 1 minute can be used to calculate the average pressure and standard deviation for each piece of equipment.
    • State Management: Flink's native state management is key. It stores the aggregated data within the Flink job itself, ensuring fast access and fault tolerance. In case of a failure, Flink automatically recovers the state from a checkpoint, guaranteeing exactly-once processing semantics.
  • Event Correlation:
    • Complex Event Processing (CEP): Use Flink's CEP library to detect complex patterns of events across different data streams. This is crucial for root cause analysis.
    • Example Use Case: A Flink CEP job can be configured to detect a sequence of events: (1) a sensor reading for a specific chamber drifts > 3 standard deviations, (2) followed by a 'process start' event for a specific recipe, (3) and then a 'wafer test fail' event. When this pattern is detected, Flink can trigger an immediate alert to the equipment and process engineers.
  • Output & Alerting:
    • The processed data, aggregations, and alerts are written to a downstream Kafka topic (e.g., real-time-alerts).
    • A separate service or Flink job can consume from this topic and send alerts via email, Slack, or to the on-call system.

This design provides a high-speed, low-latency, and fault-tolerant solution for real-time data analysis. By using Apache Kafka and Apache Flink, you can handle the massive volume of sensor data and proactively detect issues before they impact yield.