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Predictive Maintenance in the Electronics Industry

Key Takeaways

  • Predictive maintenance enables manufacturers to proactively prevent equipment failures, reducing breakdown occurrences and improving efficiency.

  • AI-driven analysis of sensor data facilitates faster and more accurate predictions, streamlining maintenance strategies in real-time.

  • Challenges related to data ownership in complex supply chains must be addressed for the widespread adoption of predictive maintenance in electronics.

Engineers on production line conducting predictive maintenance

Predictive maintenance refers to a variety of methods all aiming to reduce potential issues in production.

The electronics and chip industry is creating more reliable devices and ICs through the concept of predictive maintenance, a revolutionary approach that enables manufacturers and engineers to detect and prevent impending failures before they lead to critical issues. Through predictive maintenance, production lines are digitized resulting in increased productivity, higher yields, and increased competitiveness. Read on as we explore how predictive maintenance is transforming the manufacturing of electronics, aided by advancements in AI and IoT technologies.

Predictive Maintenance Techniques in the Electronics Industry 




Leveraging AI algorithms to process and analyze data collected from various sensors. AI's cognitive abilities, including learning and reasoning, help identify potential issues and enable faster, more accurate predictions for proactive maintenance strategies.

On-die circuitry

On-die circuitry in modern SoCs provides real-time data on transistor properties, system workloads, and more. This data can be used to determine when components should be replaced, leading to self-healing devices and improved reliability.

Outlier Detection

Outlier detection techniques compare data from individual ICs with others to identify differences and anomalies. Engineers use outlier algorithms to flag potentially failing components, allowing for timely actions to prevent failures and assess liability risks.

Traditional Methods (WSN, logs, sensors)

Traditional predictive maintenance methods involve using Wireless Sensor Networks (WSN) and sensors to monitor machine function and health. Data from device logs and sensors are wirelessly transmitted to central computer servers for analysis, enabling manufacturers to take proactive measures and prevent failures

Understanding Predictive Maintenance

Predictive maintenance is essential for reducing breakdown occurrences, optimizing operational costs, and maximizing asset uptime.  By monitoring machine function and health using data from device logs and sensors, manufacturers can identify potential problems and take proactive measures such as repairs or replacements, effectively preventing failures. Furthermore by embracing this strategy, manufacturers can improve maintenance budgets and streamline production time, leading to increased efficiency and reliability in the production process.

How Predictive Maintenance Works 

In general predictive maintenance is rather specific to each production line. However, much of it relies on diverse amounts of data gathered from multiple sensors deployed throughout the production line. The flow of data in a predictive maintenance system is as follows: 

  • Sensor collects data: Sensors are strategically deployed throughout the production line or on the equipment to monitor various parameters and performance metrics. These sensors can measure factors such as temperature, vibration, current, and other relevant data points.

  • IoT compiles data: The data collected by the sensors is transmitted, often wirelessly, through an Internet of Things (IoT) / wireless sensor network. This network connects the sensors and devices, allowing them to communicate and share information. The IoT system ensures that the data is securely transmitted to a central computer server or cloud storage for further processing.

  • AI or algorithm analyzes data: At the central computer server or cloud storage, the collected data is processed and analyzed by sophisticated algorithms, often involving Artificial Intelligence (AI) models. These AI-driven algorithms have the capability to identify patterns, trends, and potential anomalies within the data.

Predictive Maintenance In Action with AI

Predictive maintenance involves deploying sensors to monitor various aspects, from individual components to entire smart building systems. For example, sensors can monitor current, vibration, and airflow in HVAC equipment like motors, compressors, and fans. The data collected from these sensors can be processed and analyzed using microcontrollers and security solutions, enabling these advanced IoT features.

This data can be securely sent through AI algorithms, making predictive maintenance a reality. AI's cognitive abilities, including learning, reasoning, and problem-solving, are harnessed to process and analyze sensor data, helping in identifying locations of potential issues. This seamless integration of AI and IoT facilitates faster and more accurate predictions, leading to more effective maintenance strategies.

Implementations of Predictive Maintenance in the Electronics and Chip Industry 

There are a variety of different methods enabling predictive methods being used in the electronics industry. For example, some modern SoCs (System-on-Chip) are equipped with in-circuit monitors, providing real-time data that can be correlated with expected system performance. This allows engineers to detect impending IC failures and make necessary changes before they impact the overall system's performance. In fields such as automotive and data centers, predictive maintenance is also used to schedule maintenance proactively and prevent catastrophic failures.

On-Die Circuitry

On-die circuitry can offer various measurements, ranging from transistor properties to system workloads, allowing end customers to determine when components should be replaced. Typical measurements include Device Vt shift over time, gate-to-drain capacitance uniformity, and relative device mobility differences across die and temperature. Measurement types can be categorized as physical (parametric), structural (scan test data), and functional (bus transaction data). Moreover, memory repair and logical core repair are now common, enabling self-healing devices.

Outlier Detection

Outlier detection involves comparing data from a single IC with the same ICs in other systems to identify differences and anomalies. By using outlier algorithms, engineers can flag potentially failing components and take appropriate actions. A more sophisticated methodology involves aggregating data across many chips over time and correlating changes with wafer location, process lot, and manufacturing date. This allows vendors and operators to infer the probability of chip failures and assess liability risks, with the ultimate goal of preventing failures.

The Future of Predictive Maintenance In the Electronics Industry

Automotive and cloud industries are at the forefront of leveraging predictive maintenance. As technology evolves, the goal is to create more reliable maintenance procedures, enabling the entire fleet of chips deployed in similar applications to function optimally throughout their lifecycle. Some speculative improvements include:

  • Self-Healing Devices: Advancements in on-die circuitry will lead to the development of “self-healing” electronic devices, detecting and autonomously correcting issues, leading to enhanced resilience and longer product lifecycles
  • Enhanced Supply Chain Efficiency: Predictive maintenance will extend its influence to the supply chain, enabling better coordination between manufacturers and suppliers
  • Industry-Specific Applications: Predictive maintenance will become more tailored to specific industries within electronics, such as automotive, telecommunications, data centers, and smart manufacturing

While the benefits of predictive maintenance are evident, several challenges hinder its widespread adoption. One of the major hurdles is data ownership, especially in complex supply chains where sensor data is collected from various chips supplied by different manufacturers. Resolving this issue is crucial to ensure the efficient implementation of predictive maintenance strategies.

The Future with Allegro X Advanced Package Designer 

As the electronics and chip industry embraces the concept of predictive maintenance, it is transforming manufacturing and field maintenance processes to ensure enhanced productivity, and higher yields.  As we look towards the future, the goal is to achieve more reliable maintenance procedures, enabling electronic systems to function optimally throughout their lifecycle.

To fully leverage the benefits of predictive maintenance and maintain seamless implementations of your designs, we encourage industry professionals to explore the power of Allegro X Advanced Package Designer. By integrating Allegro X into your operations, you can stay ahead in the fast-paced electronics industry and ensure the highest levels of efficiency, reliability, and competitiveness. Embrace the future of predictive maintenance with Allegro X Advanced Package Designer, and lead the way in proactive responses and optimized electronic systems.

Leading electronics providers rely on Cadence products to optimize power, space, and energy needs for a wide variety of market applications. To learn more about our innovative solutions, talk to our team of experts.