Produced by Zhineng Zhixin

With the development of technology, artificial intelligence and machine learning are becoming more and more widely used in various fields, especially in the design and testing phase, which can not only help enterprises save a lot of time and money, but also provide unprecedented insights, although there are still many challenges in the application process.

The core strength of artificial intelligence lies in its powerful data analysis capabilities. In traditional design and testing, engineers often have to deal with huge data sets, and human processing power is limited.

The introduction of AI/ML has enabled these complex tasks to be completed in less time, accelerating the entire design-to-test process.

Part 1

Benefits:

Data analysis and problem diagnosis

AI/ML enables rapid analysis of large data sets to extract key patterns and trends. For example, during chip design and testing, AI/ML can help identify design flaws and predict potential problems to avoid major mistakes in the production phase.

As technology advances, analytics and computing infrastructures become more complex, and AI/ML enables more accurate decision-making while minimizing the amount of computation. In the traditional design-to-test process, incompatibilities between tools often lead to inefficient debugging and development.

AI/ML can effectively solve these problems, ensuring seamless collaboration between the various tools. For example, by allowing all settings to use the same set of source files, AI/ML greatly reduces the problems caused by incompatibilities between different tools. Test time is critical throughout the design and production process.

With AI/ML technology, companies can dramatically reduce testing time and accelerate time to market. While the combination of AI and testing is still in its early stages, the potential is huge. Through analytics, AI/ML can optimize the testing process and reduce testing time, thereby increasing productivity.

Part 2

AI/ML application challenges

There are also many challenges in practical application. These challenges focus on the following areas:

● Compatibility of tools and technologies: AI/ML can solve the incompatibility problem between some tools, but it is still difficult to achieve comprehensive tool compatibility.

There is often a lack of uniform standards between different tools and technologies, which complicates the application of AI/ML between different tools. Especially in multi-core designs, where different cores are produced at different times, trade-offs during design for test (DFT) iterations become important.

As designs become more complex, the number of cores will exceed 2,500, which means that AI/ML needs to handle a large number of variables, which places higher demands on existing tools.

● Complexity of data management and model deployment: The successful adoption of AI/ML technologies relies on large amounts of high-quality data. However, in practice, data acquisition, management, and processing are often challenging.

Data scientists often waste a lot of time on organizing and understanding the data when building and deploying new AI-based solutions, and machine learning models need to be continuously validated and tuned after deployment, a complex and time-consuming process.

● Limitations of AI: AI/ML is not a substitute for human engineering innovation, AI is just a tool, and it still needs to rely on the innovation of engineers to solve complex design and testing problems. In some cases, AI/ML may not fully solve the problem, and may even have a negative impact in some cases.

It plays a key role in the optimization and acceleration of the entire process by playing a role in a single link of design and testing.

Faced with ever-shrinking process windows and the lowest allowable defect rates, chipmakers must continuously improve their design-to-test processes to ensure maximum efficiency during equipment start-up and high-volume production. Support for real-time inference in test cells. Early adopters have already begun to build the infrastructure needed to support this process.

With protocol libraries and remote connectivity capabilities, companies can save a lot of time by reducing the development and debugging effort for device communications, using the same test sequence for characterization and testing between production, and also helping to reduce the time it takes to reproduce setups or use cases in different environments. In multicore designs, traditional serial scan test methods are often inefficient. By introducing an AI-based packet scan testing method, higher testing efficiency can be achieved.

Not only can the number of variables be reduced, but the scan channels and patterns for each core can be optimized to improve test efficiency, and by pairing the digital twin with the scan diagnostic data, the root cause of the failure can be identified more quickly, reducing the time and cost of physical failure analysis.

brief summary

The application of AI/ML in design and testing is still in the early stages, but its potential is huge, and of course it also requires enterprises to adjust accordingly in technology and management, and how to better manage and process data has become the core to ensure the effectiveness and accuracy of AI/ML models.

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