By Regan Mills, VP and GM, SOC product marketing, Teradyne
It’s no secret: the semiconductor industry is at a crossroads.
In the past, our industry could rely on Moore’s Law and Dennard scaling to continuously advance each new generation of semiconductors. But that’s no longer the case as unprecedented challenges, such as the physical limitations of scaling, have altered this once-linear path. At the same time, new trends in computing — including advanced packaging techniques (e.g., chiplets) and increased demand for more powerful processing — are making devices more complex. Add a significant skills shortage to the mix, and it’s clear that we’re on the precipice of the next evolution in semiconductor design and manufacturing.
How do we fix it? By seeking out methods and techniques that further optimize our existing solutions and processes in ways that move the industry forward.
Collaboration, A Part of the Solution
The various stages of the semiconductor lifecycle — design, fabrication and testing —have traditionally operated in silos, with limited sharing of information. Instead of directly sharing raw data and real results with one another, the information has been abstracted into specification and data sheets.
For example, a chip designer may have simulated their original design in detail. However, instead of directly sharing the simulation results with other groups, they’ll conventionally distill that information into a specification sheet — which is the only information that is passed down the line.
And that’s problematic because many times specification sheets don’t capture all the granular detail, so significant information is lost. Because this lack of transparency obscures important details, it’s been difficult for the semiconductor industry to fully optimize designs and processes.
The Role of Data
Fortunately, change is in progress. Advanced analytics platforms that rely on sophisticated ML and AI models are enabling every part of the semiconductor value chain to take advantage of new methods for analyzing and acting on the vast amounts of data available during the design and manufacturing process.
On one hand, sharing can work in the forward direction, with each subsequent stage in the lifecycle receiving data from the previous stage. If testing groups could access simulation results, they’d be better informed on the tolerances and margins required for their test setups. This would produce more accurate and reliable data, resulting in higher-quality devices and a positive impact on yield.
Sharing feedback is also integral to collaboration. Consider what happens when a product fails in the field. Here, sharing lifetime and diagnostic data from the device could help to identify which stages in the lifecycle led to the failure. This feedback could then be integrated to improve processes, leading to better-designed and higher-quality end devices.
By way of analogy, imagine a fleet of electric vehicles (EVs), each equipped with advanced sensors that collect data on battery life, motor efficiency and overall vehicle performance under various conditions. If one EV in the fleet experiences a failure, the EV’s communications system could share the data collected up to the point of failure with the automotive manufacturer. This shared information would allow the manufacturer to diagnose the cause of the failure, whether it’s a flaw in battery design, an issue with the electric motor or another problem in a different subsystem.
In the same way, the semiconductor industry can leverage data to identify flaws in processes, the resolution of which will lead to quality, yield and efficiency gains across the board.
Climate for Collaboration
It’s clear that sharing information has huge potential for improving processes in the semiconductor industry. Fortunately, the climate for collaboration has never been better. With governments around the world supporting their own versions of CHIPS Acts, funding and resources in the semiconductor industry are at an all-time high. This groundswell gives the entire semiconductor industry the chance to benefit from the momentum.
Simply interfacing, however, is not enough. We need well-defined standards, whether those are standard file formats for different aspects of the semiconductor lifecycle or a standard means of sharing data that allow every player in the value chain to maintain their differentiation and competitive edge while enabling interoperability.
Peripheral Component Interconnect Express (PCIe) is a great example of how standards can improve not only technical performance and efficiency but also differentiation within a well-defined system. PCIe, a high-speed, serial bus standard, is the common interface between motherboards, and PC hardware and peripherals. PCIe provides lower latency and higher data transfer rates than previous standards, such as PCI. Industry adoption of this standard has ensured that companies could create differentiated products based on the intended application with the confidence that the components would be interoperable.
The semiconductor industry needs to continue this evolution, prioritizing the development of new data standards that will benefit the semiconductor manufacturing ecosystem as a whole.
Change is already underway. SEMI’s Smart Data-AI Initiative exemplifies a new approach to industry collaboration as it aims to provide a framework for sharing data among different functions within a fab.
“The global semiconductor industry is projected to reach $1 trillion by 2030, according to a 2022 report from McKinsey & Company, but this will not happen on ‘auto-pilot.’ To accomplish this, we will need to continue the pace of innovation to make billions of increasingly complex microelectronic devices — all of which must be tested for performance, reliability and other metrics before they reach their target application,” said Dr. Pushkar Apte, strategic technical advisor, SEMI. “If we are to maintain high performance and quality on such a massive scale, we need to embrace the strategic integration of data analytics, machine learning and AI in semiconductor manufacturing processes. SEMI’s Smart Data-AI Initiative provides a platform to drive value-creation from data and AI that are specific to the semiconductor ecosystem. The initiative enables pre-competitive collaboration through the entire ecosystem to accelerate innovation while preserving the integrity of an individual company’s IP.”
With this foundation beginning to take shape, how do we handle the resultant influx of data analytics?
At Teradyne, we facilitate sharing through analytics solutions that are based on an open architecture. This approach lets our customers easily integrate off-the-shelf data analytics solutions from third-party companies with our testers. And because our architecture is agnostic, customers can also use the same open architecture with their home-grown analytics solutions. The choice is theirs.
Beyond Moore’s
The physical aspects of Moore’s Law are decelerating, but that doesn’t necessitate a slowdown in semiconductor advancements. In this era where collaboration is taking on an increasingly important role in the semiconductor industry, the opportunity for new paradigms is plentiful, but it’s up to us to evolve the way the industry works together.
Regan Mills is the vice president and GM SOC product marketing, Semiconductor Test division at Teradyne. Prior to Teradyne, Regan held management positions at Automation Engineering Incorporated and Arctic Sand Technologies. He holds a Bachelor of Science degree in electrical engineering and computer science from the Massachusetts Institute of Technology, and a Master of Science degree in electrical engineering, control systems, digital signal processing and analog design from Boston University.
