Volpiano (Italy)
October 29, 2024
Mixed Signal Testers for AI Microchips: What You Need to know
Microchips are the backbone of artificial intelligence (AI) applications, powering everything from self-driving cars to virtual assistants. AI chips are designed to process large amounts of data and make decisions in real-time, making them crucial for the success of final applications.
As AI continues to revolutionize industries, the demand for more powerful and efficient microchips to support these applications is on the rise.
However, with the growing complexity of AI algorithms and the need for faster processing speeds, testing these microchips has become a major challenge for semiconductor companies. Without proper testing, these chips may not function as intended, leading to errors and potentially dangerous consequences. This is especially true for applications that require real-time decision making, where any errors or malfunctions can have serious consequences.
In this article, we will explore the challenges in testing microchips for AI applications and the evolving capabilities required of mixed signal testers for robust AI microchip validation.
Complex AI chip designs present specific test requirements
One of the main challenges in testing microchips for AI applications is the complexity of the algorithms they are designed to support. Modern AI chips often integrate diverse processing elements like CPUs, GPUs, specialized AI cores, and rely on a combination of digital and analog signals. This combination necessitates tests that cater to each functional block while ensuring seamless interoperability. By incorporating these advanced features, the new generation of mixed signal testers can effectively and efficiently overcome the challenges posed by the ever-growing complexity of AI chip designs.
High-precision analog and digital resources
To handle the ever-increasing complexity and pin counts of AI chips, mixed signal testers should excel at testing both analog and digital circuits with high precision. This means testers should be equipped with a variety of features and capabilities, including high-speed digital and analog testing, low-noise signal generation and analysis, high-speed digital pattern generation, and advanced data processing.
Large bandwidth requires high-speed digital channels and memory depth
To keep pace with the evolving architecture of AI chips, mixed signal testers need to boast increasingly sophisticated capabilities. As chip designers leverage technologies like 3D-stacking to enlarge bandwidth and facilitate the transfer of massive datasets in record time, new-generation testers must adapt accordingly. This necessitates a large quantity of high-speed digital channels, capable of handling frequencies ranging from 400MHz to tens of GHz. Additionally, large memory depth is crucial for accommodating the extensive test data these complex chips require.
Intricate chip architecture demands for tester’s distributed intelligence
The intricate multi-die/multi-core architecture of AI chips presents another challenge. Neural processing units (NPUs), for instance, often incorporate multiple cores. To effectively test these chips, mixed signal testers require a new level of intelligence. Distributed intelligence based on a multi-core architecture enables the tester to perform multiple, simultaneous computations in an asynchronous manner. Imagine a tester with its instruments and components functioning as independent, intelligent modules capable of launching test patterns autonomously. This distributed processing power significantly enhances test efficiency and streamlines the validation process for complex AI chips, enabling the tester to mimic intricate real-world operating conditions.
Smart mixed signal testers boost efficiency and speed
Another challenge in testing microchips for AI applications is the need for speed and efficiency. As AI applications become more prevalent, the demand for faster processing speeds and lower power consumption is increasing.
This puts pressure on semiconductor companies to develop microchips that can meet these demands while still being thoroughly tested. Traditional testing equipment and methods can be time-consuming and may not be able to keep up with the pace of development, leading to delays in bringing products to market. To keep up with efficiency, mixed signal testers should be capable of high multi-site capabilities, and should incorporate specific features aimed at maximizing the test execution speed:
- Distributed intelligence is a game-changer: multiple CPUs embedded within the tester and its instruments, all working simultaneously, allow the tester to run multiple test processes concurrently, significantly accelerating test execution.
- Multi-time domain capability is essential for maximizing test speed. This feature enables the tester to run digital signals with different time domains at the same time. This translates to testing concurrently the various blocks within the chip under test, decreasing dramatically the overall test-time
- Embedded Digital Signal Processing (DSP) units on both analog and digital instruments contribute to test time optimization. These on-board DSP units perform data de-coding and computations directly on the instruments, eliminating the need for data transfer back and forth to a central processing unit, which can slow down the testing process.
- Protocol-aware instrument architecture is crucial. By understanding the communication protocols used by the DUT, the tester can streamline pattern complexity and optimize communication efficiency, further accelerating test execution.
Monitoring the Chip Power Consumption ensures accurate energy management
A hallmark of AI chips is their focus on power efficiency. This makes power management expertise one of the key characteristics that a new-generation mixed signal tester for AI microchips must possess.
Multi-domain power management to test complex chips
These chips often boast high-density layouts, integrating diverse processing elements with their own specific power requirements. This translates to a multitude of power domains, each requiring meticulous verification. The tester needs to be adept at precisely controlling and monitoring power delivery across these domains, not just at the system level, but also for individual sections of the chip. This granular power management ensures that the chip operates under realistic conditions, allowing for accurate power consumption verification and the detection of power-related defects that might otherwise go unnoticed.
Power consumption verification on AI microchips
Furthermore, the immense processing power required by AI chips makes them inherently energy-consuming. This high-power draw translates to significant energy costs and thermal challenges for data centers, causing headaches for IT managers. To address this concern, mixed-signal testers must be equipped with a robust suite of power supplies capable of accurately stimulating the AI chips under test at various operating points. This enables comprehensive profiling and verification of the chip’s power consumption behavior, ensuring it meets design specifications and contributes to a more energy-efficient overall system. By closely mirroring real-world power conditions, testers can help mitigate the data center power consumption woes associated with AI deployments.
Fluctuating power consumption needs for high-current, precise analog channels
Additionally, the dynamic nature of AI chip operation necessitates new-generation testers with a suitable number of high-current analog channels. Unlike traditional chips with steady power demands, AI chips exhibit fluctuating power consumption as workloads change. The tester’s analog channels need the capacity to deliver these high currents while maintaining precise control. Fast instrumentation is equally important for effectively modulating the current supplied in response to the chip’s real-time requirements. This ensures that the chip receives the exact amount of power it needs at any given moment, mimicking real-world operating conditions and enabling comprehensive power integrity testing.
AI-powered test development streamlines time and resources
One of the most promising solutions for testing microchips for AI applications is the use of artificial intelligence (AI) itself.
Leveraging AI for test program generation and defect analysis can significantly improve efficiency and effectiveness. Imagine an AI that can learn from past test data to suggest optimal test sequences and parameters and pinpoint root causes of failures: that would be a true game-changer to streamline the test development.
This is crucial especially for companies that need to test a large number of applications, as it can save time and resources. With the ability to automate test development operations, test engineers can focus on other important tasks, such as analyzing data and improving performance.
AI can also help with the speed and efficiency of testing, as it can quickly analyze large amounts of data and identify potential issues. This can help companies keep up with the pace of development and bring products to market faster.
Conclusion
Testing microchips for AI applications is a crucial step in the development process, but it comes with its own set of challenges. The complexity of AI algorithms, the need for speed and efficiency, and the importance of power consumption monitoring all make testing these microchips a difficult task.
Investing in the right test equipment is essential to maximize AI chip performance and stay ahead of the competition in this rapidly advancing field.
The new generation of mixed signal testers, as the SPEA DOT800, includes features like:
- High-speed digital and analog testing capabilities
- Large memory depth
- Distributed intelligence on a multi-core architecture
- Multi-time domain operation
- DSP units on analog and digital instruments
- Protocol-aware instrumentation
- A robust suite of power supplies
These advancements enable mixed signal testers to mimic real-world operating conditions, perform comprehensive power integrity testing, and streamline test execution.
Furthermore, the integration of AI into the testing process holds immense promise. AI-powered test program generation and defect analysis can significantly improve efficiency and effectiveness, allowing test engineers to focus on higher-level tasks.
By embracing these advancements, semiconductor companies can ensure the robust validation of AI microchips, paving the way for the next generation of groundbreaking AI applications. The future of AI microchip testing is bright, characterized by continuous innovation and a symbiotic relationship between cutting-edge chip design and powerful testing methodologies.
