Wave Scale RF8: Ready for Wi-Fi 7

By Jan Ermert, Product Marketing, Advantest Europe

Since Advantest launched the Wave Scale family of test cards for the V93000 system-on-chip (SoC) test platform five years ago, we have continually added new products and capabilities to the line, allowing us to address new, emerging test demands. The Wave Scale RF8 card addresses the test challenges associated with the forthcoming Wi-Fi 7 standard by providing the bandwidth needed in an industry-proven instrument. Wi-Fi 7 covers the (so far, for Wi-Fi) unused frequency range between 6 GHz and 7.125 GHz, using up to 4096-QAM modulation schemes and up to 320MHz channel bandwidth (see Figure 1).

Figure 1. Wi-Fi band ranges are shown here, including the 3x increase in bandwidth enabled by advanced Wi-Fi. 

Figure 2 shows the Wi-Fi standard roadmap. With anticipated Wi-Fi 7 certification just two years away, customers need to plan their testing strategy now, integrating Wi-Fi 7 ready technology so that it is already in place when they are ready to begin rolling out Wi-Fi 7-certified products.

Figure 2. Current Wi-Fi 7 standardization, certification and commercialization timelines. [Source: “IEEE 802.11be: Wi-Fi 7 Strikes Back,” IEEE Communications Magazine, Volume 59, Issue 4, April 2021] 

Wi-Fi 7 application drivers

Wi-Fi 7 is optimized to ensure the highest possible throughput and the lowest latency. This comes in handy not only for entertainment apps but also for enterprise users and for virtual reality (VR) and augmented reality (AR) implementations. To this end, Wi-Fi 7 includes improvement for the different layers of the Open Systems Interconnect (OSI) model. Specifically, we’re looking at the physical layer, which determines how the data is transmitted and organized. It also defines how we deal with all aspects of an RF interface with regard to sharing a finite amount of spectrum between many access points and end points.

Many of these aspects will be addressed by new capabilities within Wi-Fi 7 releases 1 and 2. One of the key features being improved on is the introduction of even wider channel bandwidths in the relatively new 6GHz band. Release 1 proposes 320MHz and smaller channel bandwidths with up to 4096 QAM modulation – a major challenge for measurement equipment that is addressed by our Wave Scale RF8 solution.

The tradeoff for the impending bandwidth improvement in Wi-Fi 7 is that some range or signal reach may be sacrificed. During the transmission of a Wi-Fi 7 data package, it is necessary to convert the bits and bytes within the lowest part of the OSI model, the physical layer, into a phase- and amplitude-modulated analog signal at high frequencies, which can then be transmitted over the air. For the receiver to be able to extract the digital information from the received analog signal, the noise created by the signal chain must not be interfering with the signal. A typical measurement that tests for this interference is EVM, which checks the error vector, or delta between the signal as it should be and the signal as it was received (see Figure 3). 

Within the modulation the bits are encoded by shifting a carrier frequency both in phase and amplitude in discrete steps. This is typically plotted as a grid with each point on the grid representing one possible modulation state. Each point also represents a symbol, a pattern of bits that is unique to this one spot. The amount of grid points determines how many bits can be transmitted with one symbol. With a 4096 QAM, as used in Wi-Fi 7, each symbol carries 12 bits.

The denser this grid becomes, the faster the bit transfer for the same bandwidth, or spectral efficiency of the modulation scheme. This, however, has the huge disadvantage, that any noise added to the signal might lead to misinterpretation of the received symbol. The denser the grid pattern, the higher the sensitivity for noise. For a 4096 QAM, the expectations regarding a receiver are set very high, which, in turn, means the same for the measurement equipment measuring the receiver. For Advantest, this means that in order to provide a margin that guarantees the test results to be reliable, our test equipment needs to be multiple decibels better in EVM measurements than the DUT, and we’re well prepared to meet this challenge.

Figure 3. Example of a 16 QAM modulation scheme (l), EVM explanation, and calculation (r)

The Wave Scale solution

Wave Scale RF8 can perform both highly parallel multisite and in-site parallel test, testing both the send and receive channels in a fraction of the time required using a traditional test flow. It can also perform high multisite testing using native ATE resources, all within the V93000 test head. The card offers multiple benefits for Wi-Fi 7 test – in addition to support for 320MHz 4096 QAM, these include best-in-class EVM performance, wide operating frequency (10MHz to 8GHz), and scalability for in-site parallelism, among others (Figure 4).

Figure 4. Wave Scale RF 8 delivers unmatched parallelism with best-in-class EVM performance for Wi-Fi 7 testing.

Due to Wave Scale RF’s flexible architecture, high-performance/ultra-low-noise signals are provided by a Precision Source card when needed. This provides for a better driver signal, further improving the EVM for Wi-Fi 7. Wave Scale RF8 features a source combiner that couples two of its four independent synthesizers, doubling the 160 MHz bandwidth to achieve 320 MHz on the stimulation side and thus enabling the card to perform Wi-Fi 7 testing (Figure 5).

Figure 5. Each of the card’s RF subsystems functions as an independent instrument for highly parallel processing, enabling all ports to achieve 320MHz. 

Implementing this solution requires no additional accessories, nor any additional components on the load board or special test head cabling. This means that for existing Wi-Fi 6/6E configurations, there is no change necessary if the load board components support the frequencies and bandwidths for Wi-Fi 7. Wave Scale RF8, together with the SmarTest8 ATE software, provides best in-class Wi-Fi 6E testing today and is ready to accommodate your Wi-Fi 7 DUTs as they are developed and readied for production. 

As the technical ATE leader in Wi-Fi test, Advantest is committed to continually staying ahead of the technology curve. We have a track record of developing elegant, easily integrated solutions that anticipate customers’ needs for Wi-Fi testing capabilities. By building a rock-solid platform with a dedicated RF instrument card that offers high performance and flexibility, we are already supporting future standards and will be ready for Wi-Fi 7 when you are! 

Industrial Solutions for Machine-Learning-Enabled Yield Optimization and Test

This article summarizes the content of a paper developed and presented by Advantest at ETS 2022.

By Sonny Banwari, Vice President, Advantest Cloud Solutions, and Matthias Sauer, Applied Research Project Manager, Advantest Europe

According to market research firm Gartner, Inc., in assessing the completion rate of data science projects, as well as the bottom-line value they generate for their companies, only between 15 and 20 percent of these projects are ever completed. Moreover, of those that do manage to reach completion, less than 10 percent of them generate value, according to feedback provided by corporate CEOs. The bottom line: less than 2 percent of data science projects ever get completed AND deliver value. How can this squandering of corporate investment and effort be alleviated? One way is through the use of advanced machine learning (ML) techniques.

However, implementing ML in online manufacturing test poses its own set of challenges. ML-based applications challenge traditional test flows and infrastructures, as they require:

• Large amounts of data, often through multiple insertions spread geographically across multiple continents and located at various corporate entities depending on the position in the value chain;
• A secure, scalable and integrated compute infrastructure based on open standards; and
• A dynamic test execution infrastructure.

Some of these properties actually conflict with traditional test setups, which results in non-standard test flows and creates extra work that impacts time to market and return on investment – and, notably, slows success and adoption of ML applications.

Repeatability and reproducibility are essential to test procedures, particularly for automotive and other markets that rely on standardization and a high degree of compatibility. This requires establishing more structured thinking around ML and its impact on test. Figure 1 illustrates an industry-ready ML lifecycle designed to bring data science to the test floor.

 Figure 1. The four key steps of an ACS-powered machine-learning lifecycle are shown here.

ACS enables the ML ecosystem

Advantest Cloud Solutions (ACS) is a highly secure scalable data platform enabling an open solution ecosystem that helps customers address the most pressing challenges of the Smart Manufacturing era. The open aspect of the ecosystem is essential, as it allows any company along the supply chain not only to use it but to add value, create partnerships, deploy their own solutions, etc. ACS provides the vital infrastructure piece, as well as a wide range of development offerings through the ACS Solution Store, while Advantest provides other software products and services that customers can purchase when they need supplemental services to augment or enhance their existing deployments. Let’s take a brief look at each of the four key steps of the ML lifecycle.

Problem exploration and understanding

The earlier users seek to identify problems in the manufacturing process, the more data they require. Early bad-die detection is a vital component of this effort, as predicting likely failures post-packaging can help to significantly reduce costs and improve quality in the packaging process. To achieve this requires large amounts of high-quality data. Figure 2 shows the traditional test flow without benefit of ML techniques at left. At right, our ACS technology assesses data gathered from prior insertions and correlates it to accurately predict problems, enabling the user to circumvent them by fixing problems at the root cause, thus preventing bad die from reaching downstream test insertions. This reduces not just the cost of test but also the cost of the materials and processing needed as chips travel through the three-month long manufacturing process across continents and companies.

Figure 2. Using Advantest ACS techniques, customers can omit bad die early in the test cycle to reduce packaging costs and improve quality.

Model engineering

Model engineering is a crucial step for implementing assessed business requirements and turning them into a data-driven ML application, either using a custom implementation or employing pre-defined solution from the ACS Solution Store.

To evaluate the “ACS Yield Optimization” Reference App described above, we compiled a real-world dataset containing more than 200 relevant rest results per die from probe test and multiple fail bins from final test.

Running ACS-driven data analytics on the compiled, de-duped data, using one device under test (DUT) ID per entry, the tool uses deep learning-based variable selection to determine the variables of greatest influence on yield. It then creates new probe test limits based on the distribution of this data, removing false passes and confirming yield improvement. In the aforementioned case, the result was a 5% improvement in yield, from 88% to 93%, which translated to six-digit savings per year in U.S. dollars.

Figure 3. ACS Yield Optimization uses deep learning to analyze and optimize variable, resulting in higher yields and significant cost savings.

Deployment and execution

This refers specifically to secure, high-performance test floor integration of ACS with traceable deployments, as noted in Figure 1. Advantest purposely designed our ACS suite of tools not only for optimal results, but also for ease of use. Figure 4 shows our ACS Edge™ core product, which includes the ACS Edge Server and ACS Container Hub.

ACS Edge is a high-performance, highly secure edge compute and analytics solution that enables ultra-fast algorithmic AI decision-making with millisecond latencies during test execution. It connects to the user’s test equipment via a private, high-speed encrypted link and uses the advanced container hub to run the user’s protected applications while protecting and keeping secure the user’s data and analytics.

Figure 4. Advantest ACS Edge and available extensions help customers easily and securely integrate ACS into their test flow, enabling them to realize the full benefits of its ML-enabled capabilities.

Monitoring and validation

For models to move from the lab to volume production, they must be monitored for any unexpected behaviors resulting from changes in the product design or the test environment. The effectiveness of an optimization must be validated using real-world scenarios in order for a data science project to reach completion and contribute to a company’s overall value.

Semiconductor production is highly influenced by process variations from die to die, wafer to wafer, or lot to lot, particularly at smaller process nodes with tighter geometries that afford less room for deviation. Thus, there is an inherent risk of “silent” model degradation of when a learned process characteristic changes, potentially impacting the quality of the model (yield, test time, device quality, test escapes, etc.) ACS employs a continuous learning loop with continuous monitoring, greatly reducing this risk so that models retain their integrity.

ACS Solution Store 

Another key piece of the ACS ecosystem that helps create a unified, repeatable workflow is our ACS Solution Store, which provides ease of access to ACS real-time data infrastructure solutions and software applications. This online platform enables customers to discover, purchase and securely deploy all available ACS solutions from Advantest and a broad spectrum of analytics ecosystem partners across the semiconductor lifecycle process. In addition, the ACS Solution Store enables application developers from these partner firms to publish, promote, distribute and manage their Advantest-certified apps.

This latest aspect of the ACS offerings is vital to maintain an open ecosystem, as it facilitates access to all ACS offerings for customers, as well as giving them and our partners the ability to develop and publish their own apps. This allows sharing of new capabilities and best practices so that the capabilities of our ACS technologies can be optimally leveraged across companies throughout the semiconductor ecosystem.

We continue to expand and evolve our Advantest Cloud Solutions to meet evolving customer demands. By putting ACS in place within their test environments, customers can ensure they’re armed and ready for the future of semiconductor test.

Engineering Test Station Facilitates Post-Silicon Validation

By Adir Zonta, Advantest

The semiconductor market is evolving, with devices becoming more complex as chip designers add cores and pursue 2.5D and 3D integration strategies. This complexity presents challenges extending from design and simulation through system-level test (SLT), where a device is exercised in mission mode, booting up an operating system and running end-user code, for example.

These challenges arise from the exploding volumes of test data that must be acquired and analyzed throughout the design and test process. In addition, chipmakers are increasing the number of new product introductions (NPIs) per year as they diversify their product portfolios.

The exploding data volumes and proliferating NPIs in turn are combining to increase the need for flexible test processes and equipment, and they are straining engineering teams and their facilities. 

Worst case, a company could be faced with building a new engineering facility, either next door to an existing one or in a remote location. 

Ideally, a company would find a way to increase its capacity without expanding its engineering facilities. The arrival of first silicon presents a particular pressure point, when companies must deploy teams of engineers to perform device bring-up, pattern generation, and characterization.  

Engineering test station

Advantest is addressing the needs of such customers by augmenting its V93000 EXA Scale SoC Test System lineup, which targets advanced digital ICs up to the exascale performance class while lowering cost of test and shortening time to market.

Advantest’s latest offering in the family is the V93000 EXA Scale EX Test Station—a state-of-the-art engineering platform for complex device bring-up that supports structural and functional test.

To enable a fourfold increase in tester capacity within the same engineering lab footprint, the EX Test Station fits under Advantest’s single-site M4171 automated handler, which brings automated device loading, unloading, and binning into the laboratory environment.

The handler features integrated active thermal control (ATC) over a -45 to +125°C range. ATC supports fast data collection and can improve temperature cycle testing throughput by 40% compared to manual thermal-control approaches. The handler also can include a camera to facilitate remote work and 24/7 availability. The total test-cell footprint measures 0.56 m by 0.82 m, allowing six test cells to fit comfortably within a 5-m by 5.5-m laboratory space (Figure 1).

Figure 1. Typical engineering layout of EX Test Stations with M4127 handlers.

To further save space, three, six, or nine EX units can share a single cooling unit. Furthermore, the tester, handler, and thermal-control unit form a highly automated combination to conveniently and quickly gather the high-volume data needed for today’s successful silicon bring-up activities without continual intervention by engineers. Recent experience with remote work has demonstrated its feasibility for test-engineering applications. Rather than having a complete team of engineers on site, a single operator can be available to provide hands-on assistance to address any issues observed by the engineers working remotely.

The EX Test Station employs Advantest’s Xtreme Link technology, designed specifically to provide high-speed optical data connections, embedded computing power, and card-to-card communications for ATE.

Although not intended for use in high-volume manufacturing (HVM), the EX Test Station does have full throughput capability and is therefore suitable for testing initial engineering batches efficiently. In addition, it helps to ensure seamless flow between the engineering and HVM environments. To ensure a smooth transition to Advantest’s V93000 production-test system with perfect correlation, the EX Test Station includes a V93000-compatible DUT board and uses V93000 SmarTest 8 software as well as V93000 instruments (Figure 2).

Figure 2. The EX Test Station with V93000 DUT interface and three universal slots.

Specifically, the EX Test Station accommodates up to three EXA Scale cards, including Advantest’s XPS256 Extended Power Supply (XPS) device power supply (DPS) card, which delivers 256 channels of power at current ratings in the thousands of amperes at voltages below 1 V.

In addition, the EX Test Station accepts the Pin Scale 5000 digital card, which achieves 5-Gb/s speeds and is designed to address the explosion of scan data volumes inherent in large digital designs.

The EX Test Station also works with members of Advantest’s Link Scale™ digital-channel-card family for the V93000, which enable software-based functional testing of advanced semiconductors. Link Scale cards also support USB/PCI Express (PCIe) scan testing and address testing challenges that these high-speed interfaces present. During test, the Link Scale cards communicate with the DUT through the USB or PCIe interfaces running in full protocol mode. This approach tests a device in its normal mode of operation using firmware and drivers similar to those in the target application—thereby adding System-Like-Test™ capabilities to V93000 EXA Scale systems, including the EX test station. System-Like-Test enables tests that might otherwise be applied at SLT to shift left to an ATE system.

The Link Scale cards also enable the reuse of pre-silicon functional tests by leveraging the Portable Test and Stimulus Standard (PSS), which is supported by major electronic design automation (EDA) tools and which significantly improves test quality and reduces time to market.

Finally, the EX Test Station accommodates a utility card and includes a module with 64 utility lines and a 5-V utility supply. (An external utility power supply is optional.)

The cost-optimized EX Test Station with engineering cart, integrated power supply, peripherals, and shared cooler helps control facility costs through its 4x improvement in engineering floor capacity and by reducing power requirements—it operates on single-phase, 200- to 230-V, 30-A power.

Conclusion

The drastic increase in test-data volume coupled with a proliferation of NPIs is spurring on an increasing demand for investment in engineering. Advantest’s EXA Scale EX Test Station represents a cost-effective optimized solution that performs structural and functional test to support the bring up, debugging, and characterization of complex digital devices. Combined with the M4171 handler, it forms a highly automated engineering test cell with remote-access capabilities. The complete test cell offers a fourfold increase in engineering-lab capacity without any increase in footprint while minimizing engineering costs and cutting time to market.