Advantest sponsored, exhibited, and presented at SEMICON West from July 9-11, 2019 at the Moscone Center in San Francisco, California. Advantest’s booth was centrally located in the South Hall alongside other large semiconductor companies and featured the new theme, “5G: Made Real by Our Customers, Made Possible by Advantest.”
In Booth 939, Advantest showcased several products including the new, and “Best of West” award finalist, V93000 Wave Scale Millimeter solution, the industry’s first integrated and modular multi-site millimeter-wave (mmWave) ATE test solution to cost-effectively test 5G-NR mmWave devices up to 70 GHz. Other displays included information on the new Advantest Test Solutions (ATS) for SoC system-level test; the MPT3000 platform for solid state-drives (SSD); T5500-series and T5800-series memory test platforms; and an array of software tools and services to improve overall productivity and test quality. The booth also contained a unique automotive display illustrating how the T2000 series of testers is improving the performance and reliability of broader types of automotive devices, from sensors, processors and powertrains to communication systems.
In addition to having a presence on the show floor, Advantest was a sponsor of the Test Vision Symposium and presented during two of the sessions. Kotaro Hasegawa, system planning senior director, presented a paper titled, “New SiP Packaging Trends and Testing Challenges” during the Packaging and Test session and then Adrian Kwan, senior business development manager, took part in an interactive panel discussion about how 5G has changed the way devices are tested titled, “Addressing Challenges of 5G Test Today and in the Future.”
On the evening of Wednesday, July 10, Advantest customers and industry members gathered for the annual Advantest Customer Hospitality Event hosted at first-time venue Local Edition, a subterranean cocktail bar in the depths of the historic Hearst building. Over 200 attendees networked to the backdrop of live music by classically trained violinist Gabi Holzwarth.
Throughout the conference, Advantest sponsored and participated in the award-winning SEMI High Tech U program, which gives high school students the opportunity to explore the semiconductor industry and develop skills in science, technology, engineering, and mathematics (STEM). Advantest employees led modules on engineering design challenges, critical thinking, and social media; conducted mock interviews; and fielded industry questions during booth tours.
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Advantest’s new MPT3000ARC is the industry’s first test platform to combine thermal-control capability with high throughput, enabling extreme thermal testing of solid-state drives (SSDs). Adding this new system to the MPT3000 product family, which is already in wide use by SSD manufacturers, Advantest is supporting SSD testing from design to manufacturing, providing the fastest, lowest risk path to market for next-generation devices, including PCIe Gen 4. In addition to meeting automotive thermal test standards, the new tester’s automation-ready thermal chamber enables SSD manufacturers to quickly ramp temperatures, which optimizes Reliability Demonstration Test (RDT) and results in faster time to market. With the addition of the MPT3000ARC, the MPT3000 series enables rapid changeover to provide a single-system test solution for a wide variety of SSD products, from 40-mm M.2 memories to larger EDSFF devices.
The MPT3000ARC’s unmatched resourcefulness is a key advantage in the continually shifting and developing SSD market, designed to enable mission-critical testing across a broad range of SSD form factors and protocols. The single-system solution allows SSD manufacturers to easily evolve from testing PCIe Gen 3 devices to Gen 4 devices by simply changing a board and downloading firmware. This new tester provides the fastest path to bring PCIe Gen 4 SSDs to market while also minimizing risks, reducing test development time and accelerating new product validation, debugging and production tests.
The continuing growth projected for the solid-state drive (SSD) market requires device manufacturers to find a highly flexible test solution capable of supporting their expanding product portfolios at a low cost of test. Advantest’s new MPT3000ARC s tester is designed with the full spectrum of capabilities to handle all SSDs, including not only the most advanced PCIe Gen 4 memories, but also the highest performing enterprise drives and the most cost-effective client devices used throughout mass-market connected devices, from smart cars to wearable electronics.
With an increasing number of SSDs being used in rugged thermal environments, these memory devices must be proven to withstand harsh conditions. The MPT3000ARC features an innovative thermal chamber that allows it to operate over a broad range of temperatures, satisfying automotive and industrial thermal-testing standards. This makes the tester ideally suited for reliability demonstration testing (RDT) for the rapidly multiplying array of applications.
The MPT3000ARC applies the same proven architecture, software and performance already in wide use by SSD manufacturers worldwide. Its production-compatible ergonomics and automation-friendly chamber access make it suitable for high-volume SSD testing.
By using changeable and customizable interface boards, this tester has the versatility to handle virtually all SSD form factors, from 40-mm M.2 memories to larger EDSFF devices. The system’s design enables quick and easy switching of interface boards, enabling rapid changeover to support a wide variety of SSD products on a single system.
As the newest member of Advantest’s MPT3000 product family, the MPT3000ARC is fully integrated. Its efficiency and performance are optimized by leveraging the same tester-per-DUT architecture, site modules, power supplies and hardware acceleration as all other systems in the MPT3000 series.
mmWave is the key topic when it comes to frequency ranges that allow to allocate more bandwidth. millimeter-wave (mmWave) is the band of spectrum between 24 GHz and 100 GHz. As it enables allocation of more bandwidth for high-speed wireless communications, mmWave is increasingly viewed as one key to making 5G connectivity a reality. In this issue, Dieter Ohnesorge, product manager, RF solutions for Advantest, discusses the market opportunity and test challenges associated with 5G mmWave, as well as Advantest’s solution for addressing them.
Q. We’ve been hearing about the promise of 5G for a long time. What demand drivers are edging it closer to fruition?
A. If you look at the global ecosystem [Figure 1], there is massive potential for 5G in many vertical markets. For example, 5G will be an essential aspect of smart manufacturing (SM). SM processes provide greater access to real-time data across entire supply chains, allowing manufacturers and suppliers to manage both physical and human resources more efficiently. This will result in less waste and system downtime and will make more technology-based manufacturing jobs available.
Remote access to health services is another key benefit of 5G. First, it would mean less driving, which is much better for the environment as well patients and doctors and staff. Second, if you’ve already had a screening and the doctor has access to it, why not communicate remotely, saving time on both sides? With 5G, you have the benefit of high bandwidth and low latency, which is important for many applications. Autonomous driving, consumer multimedia applications, and remote banking are just a few more of the many areas that will benefit from highly reliable connections, as well as high bandwidth and/or low latency.
Figure 1. A global ecosystem of vertical deployments stand ready to benefit from 5G.
Q. What has prevented 5G from becoming fully implemented?
A. Primarily, the infrastructure requirements. A specification of this scale cannot be implemented on a local basis alone – it takes a concerted, global effort. The worldwide effort to achieve 5G standardization is a huge step forward. In the U.S., discussions about mmWave technology are currently under way, and at the end of the year or early next year, the discussion will expand towards 5G in the <6GHz band.
In 2015, Verizon took it upon themselves to define a proprietary version of 5G as the next step forward from the current 4G LTE standard. At the end of 2018, the 5G NR (New Radio) industry standard developed from the Verizon effort was released, and all new deployments will follow this spec. In the U.S., initially the frequency band is 28 GHz, with carrier bandwidth of two 425-MHz channels and 24 GHz with seven 100 MHz channels. Additional frequency bands will be auctioned by the FCC for 37, 39 and 47 GHz from December 2019 onward. Other mmWave activities can be seen all over the world, although at different pace.
Q. Where does mmWave come into play?
A. Because the portion of the spectrum that mmWave covers is largely unused, mmWave technology can greatly increase the amount of bandwidth available, making it easier to implement 5G networks. Lower frequencies are currently taken up with the current 4G LTE networks, which typically occupy between 800 and 3,000 MHz. Another advantage is that mmWave can transfer data faster due to the wider bandwidth per channel, although over a shorter transfer distance – up to around 250 meters, or just over 800 feet. This means that it could conceivably work as a replacement for fiber or copper wire into homes and businesses, and this “last mile” capability would broaden the reach of 5G to cover both small and very large areas.
Q. What are the challenges around mmWave test that spurred Advantest to develop a solution? Which does it address?
A. Advantest’s Wave Scale RF card for the V93000 tester platform has seen great success. Its operational range is 10 Mhz to 6 GHz, so we needed a solution that can address the frequency and power requirements associated with higher-bandwidth devices.
Frequency is one of the key parameters associated with mmWave, and with that comes power-level measurement, EVM [error vector magnitude], ACLR [adjacent-channel leakage ratio], and other aspects that all need to be addressed in the testing process to ensure they meet specifications at the wider bandwidths required by 5G-NR.
Another requirement is the number of ports – with 5G mmWave’s beamforming capability, testing could easily be in the range of as many as 32 to 64 ports. At the same time, due to the frequency nature of mmWave, with 5x to 7x frequency, the cost goes up as well. That’s also been one of the challenges: holding down the cost of test with a wide number of sites being tested in parallel.
The V93000 Wave Scale Millimeter test solution, which we introduced in May 2019, extends the capabilities of Wave Scale RF. It is designed for multi-band mmWave frequencies, offering high multi-site parallelism and versatility. It has two operational ranges: 24 GHz to 44 GHz for 5G mmWave, and 57 GHz to 70 GHz, which extends the product’s capabilities for the wireless Gigabit, or WiGig, era. Figure 2 shows the range of frequencies that Wave Scale was developed to cover.
Figure 2. Wave Scale RF provides a scalable platform for connectivity device test, from standard RF to millimeter-wave.
In addition, new modules can be added as new frequency bands are rolled out worldwide. The card cage has up to eight mmWave instruments, making it versatile, cost-effective, and able to perform as well as high-end bench instruments. Because it has wideband testing functionality, Wave Scale can handle full-rate modulation and de-modulation for ultra-wideband (UWB), 5G-NR mmWave up to 1 GHz, and WiGig up to 2 GHz, supporting probes as well as antenna-in-package (AiP) devices connectorized, and over-the-air testing.
Figure 3 illustrates 5G device measurements that can be achieved using Wave Scale Millimeter: power out/flatness test results. The solution’s massive parallelism allows these tests to be performed quickly and at significant cost savings.
Figure 3. This graph overlays a customer’s 8-channel transceiver power-out test results, performed over 800 MHz at 28 GHz. Wave Scale allows channel flatness to be executed in a single operating sequence, one channel after the other.
Q. When will this solution be widely needed?
A. The Industry is still learning how to test these devices. We can help customers get started now, thanks to the modularity of the solution. They can start below 6GHz and when they need the higher frequency, we can add the mmWave capability.
The bottom line is that Advantest’s platform approach is ideal for this scenario – because it is scalable and modular, we can continue to add to the product’s functionality to make it even more comprehensive. By being ahead of curve, we will have the right solution ready when our customers need to adapt to new requirements.
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By Keith Schaub, Vice President of Business Development for US Applied Research & Technology, Advantest America Inc.
Introduction
Increasingly complex chipsets are driving corresponding increases in semiconductor test system hardware and software. Artificial intelligence offers innovative, ingenious opportunities to mitigate the challenges that test engineers and test-system operators face and to improve security and traceability. Advantest, which fields thousands of test systems worldwide that test billions of devices per year, is studying several ways in which AI can help.
Initial work has involved facial recognition and overlapping speech transcription (the latter being the focus of this article), both of which can reduce the need for a mouse and keyboard interface. With a mouse and keyboard, operators can leave themselves logged in when other operators take over, creating security vulnerabilities and making it difficult, for example, to trace which operator was on duty during a subsequently detected yield-limiting event. A voice-recognition system could facilitate identifying which operators gave which commands.
Industrial cocktail-party problem
Implementing a voice-recognition system in a test lab or production floor presents its own challenges, with air-cooled systems’ fans whirring and multiple teams of engineers and operators conversing—creating an industrial version of the cocktail-party problem.
To address this problem, Advantest has developed fast, multi-speaker transcription system that accurately transcribes speech and labels the speakers.
The three main steps in the transcription process include speaker separation, speaker labeling, and transcription. For the first step, a real-time GPU-based TensorFlow implementation of the deep-clustering model recently developed by Mitsubishi1 separates the mixed-source audio into discrete individual-speaker audio streams. A matrix of audio-frequency domain vectors obtained by the short-time Fourier Transform (STFT) serves as the input to this model. The model learns feature transformations called embeddings using an unsupervised, auto-associative, deep network structure followed by a traditional k-means clustering method (recent implementations have shown significant improvements over traditional spectral methods) that output the clusters used to generate single-speaker audio.
The second step involves an implementation of Fisher Linear Semi-Discriminant Analysis (FLD)2 for an accurate diarization process to label the speakers for each audio stream that the clustering model generated in the separation step. The third and final step makes use of the Google Cloud speech-to-text API to transcribe the audio streams, assigning a speaker based on the diarization step.
Figure 1: This system-flow diagram illustrates the steps in the overlapping speech-transcription process, from the audio input to the labeling of the speakers.
Figure 1 illustrates the system flow of the entire process. During the first step, the clustering separates the audio. The spectrogram of the mixed and separated audio (Figure 2) makes it easy to visualize the separation taking place.
Figure 2: A view of the spectrogram of the mixed and separated audio helps illustrate how the separation takes place.
Testing the model
We tested the model on the TED-LIUM Corpus Release 3,3 which is a collection of TED Talk audio and time-aligned transcriptions. To measure the system accuracy, we compared our system-generated transcriptions to the ground-truth transcriptions using Word Error Rate (WER), denoted by the proportion of word substitutions, insertions, and deletions incurred by the system. Our system demonstrated a WER of 26% versus a ground-truth WER of approximately 14%. Overall, the generated transcripts were largely intelligible, as shown by the following example:
Actual Audio
“Most recent work, what I and my colleagues did, was put 32 people who were madly in love into a function MRI brain scanner, 17 who were. . .”
System Transcription
“Most recent work but I am my colleagues did was put 32 people who are madly in love into a functional MRI brain scanner 17 Hoover.”
As shown, the results are largely readable, even with the current word error rate.
Often, the audio output from the Separation Step contains many artifacts, which lead to outputs readily understood by humans but that are more difficult for current speech-to-text converters. Thus, we get an output like this:
Actual Audio
“Brain just like with you and me. But, anyway, not only does this person take on special meaning, you focus your attention on them…”
System Transcription
“Brain, it’s like with your and name. But anyway, I don’t leave something special meeting. I’m still get your attention from you a Grande, AZ them…”
Thus, when the clustering algorithm becomes unstable, the transcription is also erroneous. However, many of these errors can likely be fixed in future work.
Overall, overlapping speech has presented a daunting problem for many applications including automated transcription and diarization. But recent innovations in learned-embeddings for speaker segmentations make it possible to produce accurate, real-time transcription of overlapping speech. The clustering model is the most computationally expensive step, but because it is implemented in TensorFlow and it is GPU-optimized, the system can run in real time. In short, recent research in learned embeddings allows for higher accuracy transcription of overlapping speaker audio.
Nevertheless, implementations of such systems are currently very limited due to relatively low accuracy, which we believe is likely the result of the clustering model using binary (discrete) masks1 to output the audio of each speaker. We will investigate continuous masking to further improve the audio quality well enough to be used for live transcription for live events.
Virtual engineering assistant for ATE
Ultimately, we envision AI techniques such as overlapping speech transcription to be useful in developing an AI-based engineering assistant for ATE, as outlined in a presentation at the 2018 International Test Conference. In the high-decibel environment of the test floor, overlapping speech transcription could help solve the cocktail-party problem, allowing the virtual assistant—a test engineering equivalent of Iron Man J.A.R.V.I.S—to respond to one particular engineer or operator.
Overlapping speech transcription is just one way of interacting with such an assistant. At Advantest, we have also experimented with facial recognition, using software that can create what is essentially a “face fingerprint” from one image, eliminating the need of traditional networks for thousands of images for training. We have found that the technology performs well at a variety of angles (photographing the subject from 30 degrees left or right, for example) and at a variety of distances (image sizes). Eventually, such technology might enable the virtual assistant to proactively intervene when recognizing a look of frustration on an engineer’s face and intuiting what information may be helpful in solving the problem at hand.
Beyond speech-transcription and facial-recognition capabilities, a virtual engineering assistant would embody a wealth of highly specialized domain knowledge, with many cognitive agents offering expertise extending from RF device test to load-board design. Such an assistant would be well versed in test-system features that might only be occasionally required over the long lifetime of expensive equipment with a steep learning curve. Ultimately, such an assistant could exhibit intuition, just as do game-playing AI machines that have mastered “perfect information” games like checkers and chess and have become competitive at games like poker, with imperfect information and the ability to bluff. Although computers haven’t traditionally thought to be intuitive, it might turn out that intuition evolves from deep and highly specialized knowledge of a specific domain.
References
1. Hershey, John R., et al., “Deep Clustering: Discriminative Embeddings for Segmentation and Separation,” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016. https://ieeexplore.ieee.org/document/7471631
2. Giannakopoulos, Theodoros, and Sergios Petridis, “Fisher Linear Semi-Discriminant Analysis for Speaker Diarization,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 7, 2012, pp. 1913-1922. https://ieeexplore.ieee.org/document/6171836
3. Hernandez, François, et al., “TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation,” Speech and Computer Lecture Notes in Computer Science, 2018, pp. 198-208. https://arxiv.org/abs/1805.04699
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By Stefan Zügner, V93000 Product Manager, Jan van Eyck, Product Owner SW R&D, Kheng How, Senior Staff Software Engineer and Daniel Blank, Senior Application Consultant Center of Expertise
Test engineers today are facing many challenges working within a collaborative test-program-development environment. Fortunately, a concept called continuous integration, or CI, can be implemented within the test-program development process to help meet these challenges.
Today, each engineer is likely to be part of a team of many engineers working on different parts of the same test program concurrently. Furthermore, test engineers developing IP blocks may be spread across widely scattered geographical locations.
The result of the complexity is that developers issue multiple program changes (commits) every day. Each commit changes the test program, and any commit may break the test program. Consequently, at any given time, the overall quality of the test program may be unknown, and problems can require significant time and resources to discover, debug, and fix. In addition, the longer it takes to discover a bug, the more time and expense it takes to fix it.
Continuous integration addresses today’s test challenges
Collaborative development typically relies on an existing source-code management system (for example, git or SVN) for tracking changes, but by the time an integrator discovers issues, it is usually too late to fix them in an efficient and timely manner. With continuous integration tooling it is now possible to trigger validation tests in an automated manner whenever changes are committed to the source-code-management repository, allowing for frequent integration and timely checks without additional overhead for the individual developer (as illustrated in Figure 1). Continuous delivery in addition automates the release-to-production process and therefore allows for new test-program releases essentially at any point in time.
Figure 1: The continuous integration workflow embraces automated validation tests for each change to a test program.
The difference between traditional and continuous integration processes is illustrated in detail in Figure 2. With the traditional process, different engineers (Alice, Bob, and Charlie in Figure 2) independently develop sections of a test program, and yet another engineer (David) performs integration and test just before the program’s release. If David’s test finds a bug, deadlines could be at significant risk because of the time it may take Alice, Bob, or Charlie to debug and rework their code and resubmit it to David for further integration and test.
Figure 2: A traditional development process (top) can put release deadlines at risk. In contrast, a continuous integration process (bottom) reduces time-to-market and time-to-quality.
The continuous-integration process, in contrast, delivers continuous and systematic validation throughout the entire development cycle, providing immediate feedback to engineers such as Alice, Bob, and Charlie on the quality of their commits. The immediate feedback made possible through continuous integration reduces both time-to-market and time-to-quality. In addition, the automated test-program validation process includes programmatic checks, which allow engineering teams to establish quality processes in a repeatable manner.
Tools for continuous integration
Several software tools can serve in a continuous-integration system.1 One of them is Jenkins (https://jenkins.io/), an open-source and widely used automation tool with support from an active community that makes information widely available on the web.
Jenkins is extensible and contains comprehensive plugins for functions such as source-code management (for example, git and SVN) and email notification.
In an implementation in which Jenkins is employed in a continuous-integration system (Figure 3), every commit can trigger the automatic running of validation jobs offline or online according to the job setup. The system stores and manages execution logs and test results while sending out notifications and reports on each execution. With the continuous-automation system automating test-program validation, test-program developers can focus on development.
Figure 3: A traditional development process (top) can put release deadlines at risk. In contrast, a continuous integration process (bottom) reduces time-to-market and time-to-quality.
Adding Smart CI to SmarTest 8
For semiconductor test-program development, Advantest offers its SmarTest 8 software for the V93000 platform.2 SmarTest 8 builds on previous versions to offer fast test-program development, efficient debug and characterization, high throughput due to automated optimization, faster time to market, ease of test-block reuse, and efficient collaboration.
To support continuous integration and delivery for test-program development in the SmarTest 8 environment, Advantest offers the Smart CI solution. The Smart CI solution includes the Smart CI custom Jenkins server plugin, which is tailored for SmarTest 8. The plugin offers simple validation job setup through “fill-in-the-blanks” forms, and it supports freestyle (GUI-based, one client) and pipeline (script-based, distributed single validation job on multiple clients) setups.
Tightly integrated with the plugin is the Smart CI Client for SmarTest 8, which provides a command line interface (CLI) to enable continuous integration and delivery for SmarTest 8 test programs. Smart CI Client can also be used for other CI solutions not incorporating Jenkins.
Also included in the Advantest Smart CI solution are Docker images for each individual SmarTest 8 release, allowing for a simplified Smart CI application. The Docker images offer preconfigured setup and enable virtual-machine (VM) and cloud installations. Multiple SmarTest 8 versions and offline jobs can also be run on the same workstation concurrently.
Smart CI works out-of-the-box
Smart CI works out of the box. Just enter a test-program name, and the program compiles, loads, and executes, comparing results against datalog and throughput references.
In addition to working out of the box, Smart CI offers Advantest templates that can be adapted with low to medium effort by a lead test engineer to validate a test program with customer-specific checker scripts. Customized results are available via offline execution.
Beyond continuous integration as enabled by Smart CI today, Advantest’s roadmap calls for the future implementation of continuous delivery, in which a test program (optionally encrypted) can be exported for production, and test-program validation can take place in a production environment, including a test cell. As such Smart CI will also offer an integration with built-in or custom release checkers of TP360.3 TP360 is a software package that helps V93000 customers increase test-program development efficiency, optimize test-program quality and throughput, reduce cost of test, and increase test-program release and correlation efficiency. TP360 is based on an open framework that enables users to add new applications easily and flexibly.
As does continuous integration, continuous delivery will work out of the box—an engineer need only enter a test-program name.
Conclusion
In summary, Smart CI enables automated continuous integration and delivery for SmarTest 8, saving test-program development time and effort and boosting engineering capacity by 10% to 15%. Smart CI ensures test-program quality through fully automated and systematic test-program quality checks throughout the entire development cycle, and it enables the release of runtime-ready test programs at any time. Furthermore, it fosters discipline in engineering teams, enabling team members to consistently deliver high quality, and it provides clear project status reports anytime, thereby increasing manageability and predictability. Smart CI Docker images simplify installation and maintenance, the Advantest Jenkins server plugin supports easy validation job setup, and Advantest provides comprehensive support and continuous enhancements.
