What are the biggest technical challenges facing automotive chips?

Vehicles are becoming data centers on wheels that need to monitor their own health to function properly. Silicon lifecycle management can provide a way to keep electric and autonomous vehicles in optimal condition.

Imagine your dream car of the future. Perhaps it's fully autonomous and can take you wherever you want to go while letting you get some work done and catch up on your favorite TV shows on the way. The vehicle drops you off when it finds a parking spot and can charge itself while it waits for you to recall it to its current location.

This description of the car of the future points to a new automotive trend whereby vehicles increasingly become data centers on wheels that need to be able to monitor their “health” in order to operate effectively, efficiently, safely, reliably, and securely. Silicon Lifecycle Management (SLM) is the answer to many of the problems that arise as we move toward autonomous electric vehicles (EVs) with more sophisticated infotainment systems.

The automotive industry is witnessing the consolidation and expansion of vehicle computing power as increasingly complex functions are being powered by EV charging systems in a variety of environments and temperatures around the world. The question is not only how we do this, but how do we know that the more advanced silicon used in automobiles is performing well and will perform well for years to come. As the average life cycle of a vehicle is expected to increase to over 15 years, these parameters become critical to scaling future updates.

SLM provides a way to monitor automotive systems-on-chip (SoCs) at many stages from testing and manufacturing to their functionality in vehicles. This data is critical to OEMs as they deploy over-the-air (OTA) updates to proactively address issues in today’s vehicles. SLM is also relevant to the next generation of software-defined vehicles as OEMs gather insight and visibility into key challenges and determine how they need to shift production to address them.

What are the biggest technical challenges facing automotive chips? The corresponding OEM difficulties, and how SLM can help solve both sets of challenges to help your next software-defined vehicle run longer, provide more convenient features, and become more resilient to security and safety threats.

Automotive SoC Challenges

New custom automotive-grade SoCs are needed to handle the centralized compute required for software-defined vehicles. As these automotive chips become smaller and more complex, the physics of these new form factors will increase the need to understand their performance.

While the entire industry is facing new challenges brought about by the accelerated expansion of device and system complexity, the challenges of automotive chips are even more complicated due to the increase in safety, reliability and security requirements. Here are the four main challenges facing automotive chip designers

Accelerating adoption of advanced process nodes: With each new technology node, transistor density continues to increase. While this density provides a great opportunity to increase technology capabilities, it also creates new challenges, such as significant variability in the manufacturing process. This widens the design envelope unless sensors and monitoring structures can be used to measure process variability across the chip. Automotive companies are now exploring multi-chip systems as a solution to overcome this scale complexity challenge.

Multi-die system adoption: As multi-die system packaging becomes more advanced, combinations of dies are “bonded” together in various configurations, from stacked dies to 2.5 and 3D packages, etc. Given this, it is important to be able to track where each die is on the process distribution.

System complexity: Data aggregation related to safety, aging and degradation, as well as power and computational throughput are all issues that stem from system complexity. In addition, future field systems will undergo multiple software updates throughout their lifecycle. If not managed properly, updated software may cause the vehicle to consume more power, shorten its service life, and negatively impact the user experience.

Increased workload: Finally, the workload of automotive chips can be unpredictable, requiring real-time optimization and application diversity, which can add more challenges and additional considerations.

Automotive OEM Challenges

Combining the technical challenges of automotive silicon with the broader OEM challenges is where the rubber really meets the road for SLM solutions. OEMs face many different obstacles and considerations when designing a vehicle and deciding how to address issues that arise during the vehicle’s life on the road.

Warranty costs and recalls: The more complex the system, the greater the chance of potential failures and the more difficult it is to resolve in a timely manner. Recalls can become a huge cost consideration for OEMs. This will also lead to a greater impact on supply chain disruptions and, as we mentioned above, may lead to more chip shortages.

Growing security challenges: As more cars receive OTA software updates, new vulnerabilities emerge that become particularly worrisome if they affect autonomous vehicles. Given these new factors, OEMs are increasingly focusing on reliability, safety, and security.

Overhaul of electrical/electronic architecture: Regional architectures are changing with new capabilities such as electrified powertrains, advanced infotainment systems, ADAS/L3+ autonomous driving levels, and overall faster release cycles.

Faster time to market: New global entrants into the automotive space are putting pressure on existing OEMs to speed up their more traditional design processes. This pressure is also having an impact on SoC supply levels and costs, global availability, etc.

Going forward, electronics will make up the most important components of vehicles and will impact the above factors and more. OEMs can no longer afford to turn a blind eye to what is happening inside a chip as this will erode profits, directly impact driver safety, and could result in missed opportunities to make their vehicles the most advanced on the market. SLM is the key to providing greater visibility into what is happening inside the vehicle and enabling the vehicle to proactively resolve issues to “self-heal.”

How SLM Meets SoC and Automotive OEM Challenges

In short, SLM solutions provide increased visibility and insight not only for fine-tuning the SoC for next-generation vehicles, but also for fine-tuning workloads on existing SoCs based on all the data collected throughout their lifecycle. A reliable SLM solution allows users to monitor issues early in the design process, transfer data to a central database, analyze the data throughout the vehicle’s lifecycle, and take strategic action whenever necessary. Ultimately, early warnings and accurate remedial actions enable the hardware to scale for future updates.

SLM allows for root cause analysis, predictive maintenance, aging and degradation alerts, and field voltage analysis, delivering real value to end customers and OEMs. In terms of predictive maintenance, chip analytics can provide more granular information for fast, accurate diagnostics. For example, an extreme temperature warning for ASIL-rated silicon could lead to a customer update to remediate via action, service, or OTA updates, which can help avoid long-term damage to critical systems and prevent large-scale recalls.

Ultimately, deploying SLM for automotive SoCs directly translates into cost reductions and savings for OEMs, increases in vehicle lifecycle value and useful life, improved reliability and troubleshooting capabilities, and relief for automotive chip shortages. Automotive SLM has been in play to some degree since chips have been used in cars, but its use cases are becoming more advanced as we move beyond mature nodes and into automotive leading-edge nodes. With more complex functionality powered by smaller chips come more challenges that SLM is poised to address. Additionally, SLM solutions can address predictive maintenance requirements that will come into play with new revisions to the ISO 26262 series of standards and ISO/SAE 21434 monitoring and analysis requirements.