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Rohde & Schwarz is accelerating the development of the new automotive Ethernet variant 10BASE-T1S by offering compliance and trigger & decode options

10BASE-T1S (IEEE 802.3cg) is a type of Ethernet networking technology that is designed for use in automotive and industrial applications, and 10BASE-T1S E2B products from ADI enable highly optimized yet flexible hardware-based Ethernet edge node connectivity solutions. The new variant is not just introducing a new lower speed grade of 10 Mbps over distances of 25 meters and beyond but also adding novel capabilities such as multi-drop transmission, thus eliminating the need for a switch. By eliminating the need for microcontrollers at the edge nodes, ADI’s E2B solution enables the centralization of all software to help enable next-gen zonal architectures and software-defined vehicles. This technological advancement is particularly applicable for linking sensors and actuators throughout the car body, powertrain, and beyond to a domain or zonal controller.

On the R&S MXO series oscilloscopes, the decoded packets are displayed in a color-coded manner, thereby making it straightforward for ADI engineers to identify crucial protocol messages such as Beacon, Commit and MAC frames. This allows for more efficient test validation, thus helping to enable shorter time to market. The 10BASE-T1S takes advantage of the new PLCA technology, making it essential to verify the timing and performance of various nodes connected onto the network. The time alignment of all decoded frames with the captured waveform simplifies debugging and facilitates precise timing measurements. ADI engineers can switch between various data representations such as DME symbols, scrambled, or unscrambled, providing valuable insights into the captured network traffic.

10BASE-T1S Ethernet technology offers additional advantages by supporting a high number of nodes on a single network, making it ideal for complex systems. Its short reach and low data-rate make it energy efficient and cost-effective. ADI’s innovative approach to 10BASE-T1S ensures that SDVs can deliver true value to OEMs by reducing form factors, cutting costs at the edge, lowering boot times, and offering bounded low latency, power saving, timestamping, and timed actuation, so that all nodes in the system are synchronized and operate seamlessly.

Fionn Hurley from ADI remarked: “ADI uses Rohde & Schwarz oscilloscopes, delivering reliable and precise measurements for our industry leading 10BASE-T1S solution. This reinforces our commitment to offering high-quality, robust technology solutions to our customers. ADI takes a holistic approach to system design and understands the complexity of the challenges faced by our customers and helps them overcome these challenges. We’re helping to drive the automotive industry towards a smarter, more efficient future.”

With the R&S MXO series oscilloscope, engineers can further leverage its high-performance capability to debug their 10BASE-T1S communication link. Standard debug tool like the spectrum mode, zone triggers and fast acquisitions makes it easy to detect anomalies on the bus. Furthermore, using the MXO’s ability to make standards-compliant 10BASE-T1S measurements in accordance with the IEEE 802.3cg and OPEN Alliance TC14 specifications, enables the automotive industry to ensure performance and interoperability of this growing automotive network technology.

The post Rohde & Schwarz pioneers the future of automotive Ethernet using Analog Devices’ 10BASE-T1S solutions appeared first on ELE Times.

AquiSense Inc of Erlanger, KY, USA (which designs and makes UV-C LED water disinfection systems) has secured Series A investment led by Burnt Island Ventures, following a recent management buyout. Additional investment comes from a local Kentucky Capital Fund and returning private seed investors led by Randy Knapmeyer. Funds will be used to accelerate growth in a broad range of water treatment applications including beverage, pharmaceutical, oil & gas and municipal...

As semiconductor devices become smaller and more complex, the product development lifecycle grows increasingly intricate. So, from early builds to pre-qualification testing, firmware development and validation teams face escalating challenges in ensuring quality and performance. As a result, traditional root cause analysis (RCA) methods—performing manual checks, static rules, or post-mortem analysis—struggle to keep up with the complexity and velocity of modern firmware releases.

However, artificial intelligence (AI) and machine learning (ML) are changing the game. These technologies empower firmware teams to detect, diagnose, and prevent failures at scale—across performance testing, qualification cycles, and system integration—ushering in a new era of intelligent RCA.

But first let’s take a closer look at RCA challenges in firmware development.

 

RCA challenges in firmware development

RCA in firmware development, particularly for SSDs, is like finding a needle in a moving haystack. Engineers face several key challenges:

• Vast amounts of telemetry and debug logs: Firmware systems generate massive telemetry and debug logs. Manually sifting through this data to identify the root cause can be time-consuming, delaying development cycles.
• Elusive, intermittent failures: Firmware failures can be sporadic and difficult to reproduce, especially under high-stress conditions like heavy I/O workloads, making diagnosis even harder.
• Invisible code behavior changes: Minor firmware updates can introduce subtle issues that conventional diagnostics miss, complicating the identification of new bugs.
• Noisy, inconsistent defect signals: Defects often produce erratic and inconsistent signals, making it difficult to pinpoint the true source of failure without extensive testing.

These issues impact product timelines and customer qualifications. AI, rather than replacing engineers, enhances their ability to detect anomalies, reduce troubleshooting time, and improve the overall RCA process, speeding up diagnosis and uncovering hidden issues.

AI-driven approaches in RCA

Below are the AI techniques that streamline the RCA process, speeding up identification of root causes and improving firmware reliability.

1. Anomaly detection: Unsupervised models like autoencoders and isolation forests detect abnormal patterns in real-time without requiring labeled failure data. These models learn normal behavior and flag deviations, helping to identify potential issues—like performance degradation—early in the process before they escalate.
2. Predictive modeling: Machine learning algorithms such as XGBoost and neural networks analyze trends in historical test and telemetry data to predict future issues, like bugs or regressions. These models allow engineers to act proactively, preventing failures by predicting them before they occur.
3. Correlation and pattern discovery: AI connects data across sources like test logs, code commits, and environmental factors to identify hidden relationships. It can pinpoint the root cause of issues faster by correlating failures with specific code changes, configurations, or conditions that traditional methods might overlook.

AI’s role in firmware validation

In firmware development—especially in NVMe devices and embedded systems—code changes can directly impact product stability and customer satisfaction. So, AI is now playing a critical role in this space.

• Monitoring I/O behavior: ML tracks latency, power, and throughput to flag regressions across firmware builds.
• Failure attribution: Historical test and return data are mined to correlate firmware changes with observed anomalies.
• Simulation: Generative models stress-test edge cases—such as power loss scenarios—to uncover potential flaws earlier in the cycle.

In an SSD development project, a firmware update intended to optimize memory management can cause subtle write workload failures during system integration. Traditional quality assurance (QA) can miss these failures, as they are intermittent and appear only under specific conditions.

However, Isolation Forest, an unsupervised machine learning model, is used to monitor real-time system behavior. The model detects timing anomalies tied to the firmware’s background garbage collection process by analyzing telemetry data, including latency and throughput. Isolation Forest identifies deviations from normal patterns, pinpointing the issues like delays introduced by changes in the garbage collection algorithm.

With these insights, engineers can root-cause and fix the issue within days, avoiding qualification delays. Without AI-based detection, there is a chance that this issue goes unnoticed, causing significant delays and customer qualification risks.

Benefits of AI-powered RCA

First and foremost, its speeds up the process by cutting debug time from weeks to hours. The AI-powered RCA also offers accuracy for multi-variable issues. Regarding scalability, it can monitor thousands of signals and logs continuously. Finally, the AI-powered RCA enables predictive action before issues reach customers.

Below is an outline of future directions for AI in RCA methods:

• Explainable AI for building trust in ML decisions.
• Multi-modal models for unifying logs, telemetry, images, and notes.
• Digital twins to simulate firmware behavior under varied scenarios.

AI is no longer optional; it’s becoming central to firmware development. On the other hand, root cause analysis is evolving into a fast, intelligent, and predictive practice. So, as firmware complexity grows, those who harness AI will lead in reliability and time-to-market.

For engineers, adopting AI isn’t about surrendering control—it’s about unlocking superhuman diagnostic capability.

Karan Puniani is a staff test engineer at Micron Technology.

Related Content

• 5 Tips for speeding firmware development
• Development tool evolution – hardware/firmware
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• Will Generative AI Help or Harm Embedded Software Developers?
• No code: Passing Fad or Gaining Adoption for Embedded Development?

The post Firmware development: Redefining root cause analysis with AI appeared first on EDN.

At the Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM 2025) Expo & Conference in Nuremberg (6–8 May), Fraunhofer Institute for Applied Solid State Physics IAF of Freiburg, Germany is presenting results achieved as part of the three-year project ‘GaN4EmoBiL — GaN power semiconductors for electro-mobility and system integration through bidirectional charging’ launched in mid-2023 and funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK)...