How Edge Computing is Transforming the Semiconductor Industry

The semiconductor industry has boomed in recent years. Statista reports that the global semiconductor market has grown from USD 412.31 billion in 2019 to USD 440.39 in 2020. Moreover, Inkwood Research predicts a 7.67 percent CAGR through 2024. That growth is driven by demand for Internet of Things (IoT), 5G, artificial intelligence (AI) and other advanced solutions in data processing, communications, consumer electronics, industrial devices, automotive manufacturing, defense, and aerospace – that all depend on chips.

Overcoming Semiconductor Manufacturing Pain Points

Semiconductor manufacturers looking for ways to increase production and process efficiency, however, often encounter challenges, including:

Miniaturization

Semiconductor manufacturers are competing to see which can deliver the smallest chipsets to meet the growing demand for more computing power in small spaces in 5G smartphones and other devices. However, the smaller objects are, the more difficult they are to package and test.

Chips on chips

The industry leverages 2.5D or 3D manufacturing processes, but when it’s preferable to integrate chips on chips, production is more difficult, requiring attention to height as well as length and width.

Machine automation

As semiconductor manufacturers automate more and more of their processes, they need to maximize uptime and meet demand. Operators also need the ability to change machines and programs frequently to produce various products, and systems need to enable the flow of real-time data to and from the edge for seamless automation.

Predictive maintenance

Semiconductor manufacturers require immediate alerts to abnormalities in equipment operations to avoid downtime and waste. The ability to maximize uptime depends on 24/7 visibility into machine health.

Employee safety

It’s vital that employees at semiconductor manufacturing facilities follow all operating procedures and wear required personal protective equipment (PPE) to work safely and protect other employees around them.

Edge Computing Enables Transformation

Semiconductor manufacturers are overcoming challenges related to a variety of use cases by implementing edge computing solutions. Examples of edge computing transforming legacy semiconductor manufacturing processes into automated and intelligent operations include:

High-precision automation

• Laser dicing: As chipsets grow smaller, manufacturers face challenges with laser dicing accuracy, precision, laser power adjustment, and the inability to provide real-time trigger control feedback. Smart machine solution is the key to solutions for laser dicing challenges, enabling visual alignment and motion control as well as quick positioning, laser power adjustments, and a solution to the problem of laser dicing following the same path repeatedly.
• Die sorting: A step in the packaging and testing process is sorting, and manufacturers need to optimize the task of separating good dies from flawed ones, then further performing QC to ensure they meet specifications. However, bottlenecks due to inadequate speed and accuracy continue to challenge the industry. An IC sorting solution leveraging -smart machine solution can provide the rapid feedback and precision necessary to improve throughput. 

Machine condition monitoring

• Dry pump monitoring: If the dry pump of a low pressure chemical vapor deposition (LPCVD) fails, it can create backpressure that pushes impure air and foreign particles into the process and contaminates the entire run of wafers. An intelligent dry pump monitoring solution continuously monitors this equipment and provides real-time data, ensuring the operation won’t experience unexpected failures and waste.
• Data acquisition: Data collection at the edge enables semiconductor manufacturers to have greater visibility into processes and equipment health and build a history of production data that can help improve overall operations or more easily meet customer reporting requirements.

AI-enabled worker safety and SOP compliance

• Cargo Tank Hazmat Offload: The semiconductor process involves a variety of hazardous chemicals, including metals, organic solvents, photoactive substances and toxic gases. Legacy offloading processes required employees to be near these chemicals, and although procedures are generally safe, an accident could threaten worker health. A robotics and edge-computing-based solution can automate processes so that employees can maintain a safe distance and ensure that all safety protocols are supported.
• Operator SOP Analysis: Robots, cobots, AI, and other automation technology more easily keep pace with the speed of production and consistent compliance with standard operating procedure (SOP) than human operators.

Partnership Will Help You Meet Demand

The recent spike in demand for semiconductor chips is unlikely to decrease – more devices, not fewer, will continue to require chips to power their continually advancing capabilities. Therefore, optimizing operations is crucial to meeting this growing demand and operating most efficiently and profitably.