The integration of advanced driver assistance systems (ADAS) and the transition towards electric vehicles (EVs) are significantly transforming the automotive industry.

Modern vehicles, essentially computers on wheels, require substantially more semiconductors. In response, carmakers are forming stronger partnerships with semiconductor vendors – some are taking a page from tech giants like Apple and Samsung by designing their own chips, often following a fabless or outsourced production model.

While a deeper connection with semiconductor design helps automakers maintain design control and supply chain resilience, it also imposes substantial responsibility to understand and meet stringent automotive quality standards.

The crucial role of semiconductor testing

Testing is vital to meet the automotive industry’s demands for quality, cost-efficiency, and timely market entry. As carmakers delve into semiconductor design, they face new challenges. Advanced semiconductors, more complex by nature, require thorough testing to ensure automotive-grade quality.

The industry’s push towards smaller process nodes, like 5nm and below, further amplifies these challenges, necessitating early and continuous engagement with testing resources to maintain high standards without compromising time to market.

Zero defects commitment

The automotive industry’s commitment to zero defects underscores the critical importance of quality. This commitment is based on an analysis of the costs associated with testing versus the potentially catastrophic costs of failures, such as life-threatening malfunctions, costly recalls, and market delays.

These issues can dramatically impact revenue and market position, highlighting the need for rigorous testing. The exceptional quality requirements inherent to automotive standards are set to intensify with the increasing digital complexity of vehicles.

Given that automotive chips must perform reliably over a lifespan of 10 to 20 years, comprehensive testing protocols play an essential role in identifying and rectifying defects early, optimizing both cost and quality. This fundamental aspect of semiconductor manufacturing cements the principle that quality is not just a priority, but the paramount concern.

This commitment transcends the capabilities of even the most skilled engineers, requiring systematic and integrated testing processes to ensure chip reliability and performance under diverse conditions.

Collaboration is key

Collaboration between automakers and semiconductor manufacturers is crucial, fostering an environment where issues can be identified and addressed early in the development cycle.

These partnerships are vital for maintaining momentum in the face of rapid technological advancements and ensuring that the automotive industry can meet the high standards of safety, reliability, and performance expected by consumers.

This collaborative approach helps to optimize testing processes, to maintain stringent quality standards, and to protect time-to-market goals, preventing production delays and ensuring the continuous advancement of automotive technologies.

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In the fast-paced world of technology, where innovation and efficiency are paramount, integrating artificial intelligence (AI) and machine learning (ML) into the semiconductor testing ecosystem has become of critical importance due to ongoing challenges with accuracy and reliability. AI and ML algorithms are used to identify patterns and anomalies that might not be discovered by human testers or traditional methods. By leveraging these technologies, companies can achieve higher accuracy in defect detection, ensuring that only the highest quality semiconductors reach the market. In addition, the industry is clamoring for increased efficiency and speed because AI-driven testing can significantly accelerate the testing process, analyzing vast amounts of data at speeds unattainable by human testers. This enables quicker turnaround times from design to production, helping companies meet market demands more effectively and stay ahead of competitors. Firms are also heavily invested in reducing costs. While the initial investment in AI/ML technology can be expansive, the long-term savings are irrefutable. With automated routine and complex testing processes, companies can reduce labor costs and minimize human error. Equally important, AI-enhanced testing can better predict potential failures before they occur, saving costs related to recalls and repairs.

The industry is now moving to chiplet-based modules, using a “Lego-like” approach to integrate CPU, GPU, cache, I/O, high-bandwidth memory (HBM), and other functions. In the rapidly evolving world of chiplets, the DUT is a complex multichip system with the integration of many devices in a single 2.5D or 3D package. Consequently, the tester can only access a subset of individual device pins. Even so, at each test insertion, the tester must be able to extract valuable data that is then used to optimize the current test insertion as well as other design, manufacturing, and test steps. With limited pin access, the tester must infer what is happening on unobservable nodes. To best achieve this goal, it is important to extract the most value possible out of the data that can be directly collected across all manufacturing and test steps, including data from on-chip sensors. The test flow in the chiplet world already includes PSV, wafer acceptance test (WAT), wafer sort (WS), final test (FT), burn-in, and SLT, and additional test insertions to account for the increased complexity of a package with multiple chiplets are not feasible from a cost perspective. Adding to the challenge, binning goes from performance-based to application-based. In this world, the tester must stay ahead of the system – the tester must be smarter than the complex system-under-test.

The ACS RTDI platform accelerates data analytics and AI/ML decision-making.

So, for these reasons and many more, the adoption of edge compute for ML test applications is well underway. Advantest’s ACS Real-Time Data Infrastructure (ACS RTDI) platform accelerates data analytics and AI/ML decision-making within a single integrated platform. It collects, analyzes, stores, and monitors semiconductor test data as well as data sources across the IC manufacturing supply chain while employing low-latency edge computing and analytics in a secure zero-trust environment. ACS RTDI minimizes the need for human intervention, streamlining overall data utilization across multiple insertions to boost quality, yield, and operational efficiencies. It includes Advantest’s ACS Edge HPC server, which works in conjunction with its V93000 and other ATE systems to handle computationally intensive workloads adjacent to the tester’s host controller.

A reliable, secure real-time data structure that integrates data sources across the IC manufacturing supply chain.

In this configuration, the ACS Edge provides low, consistent, and predictable latency compared with a data center-hosted alternative. It supports a user execution environment independent of the tester host controller to ease development and deployment. It also provides a reliable and secure real-time data infrastructure that integrates all data sources across the entire IC manufacturing supply chain, applying analytics models that enable real-time decision-making during production test.

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