Tag Archives: Automotive


The first three articles [1-3] in this series discussed methods that automotive semiconductor manufacturers can use to better meet the challenging quality requirements of their customers. The first paper addressed the impact of automotive IC reliability failures and the idea that combating them requires a “Zero Defect” mentality. The second paper discussed continuous improvement programs and strategies that automotive fabs implement to reduce the process defects that can become chip reliability problems. The third paper focused on the additional process control sensitivity requirements needed to capture potential latent (reliability) defects. This installment discusses excursion monitoring strategies across the entire automotive fab process so that non-conforming material can be quickly found and partitioned.

Semiconductor fabs that make automotive ICs typically offer automotive service packages (ASPs). These ASPs provide differentiated process flows – with elements such as more process control and process monitoring, or guaranteed use of golden process tools. The goal of ASPs is to help ensure that the chips produced meet the stringent reliability requirements of the automotive industry.

But even with the use of an automotive service package, excursions are inevitable, as they are with any controlled process. Recognizing this, automotive semiconductor fabs pay special attention to creating a comprehensive control plan for their critical process layers as part of their Process Failure Mode and Effects Analysis (PFMEA). The control plan details the process steps to be monitored and how they are monitored – specifying details such as the inspection sensitivity, sampling frequency and the exact process control systems to be used. A well-designed control plan will detect all excursions and keep “maverick” wafers from escaping the fab due to undersampling. Additionally, it will clearly indicate which wafers are affected by each excursion so that they can be quarantined and more fully dispositioned – thereby ensuring that non-conforming devices will not inadvertently ship.

To meet these objectives, the control plan of an automotive service package will invariably require much more extensive inspection and metrology coverage than the control plan for production of ICs for consumer products. An analysis of process control benchmarking data from fabs running both automotive and non-automotive products at the same design rule have shown that the fabs implement more defect inspection steps and more types of process control (inspection and metrology) for the automotive products. The data reveals that on average:

  • Automotive flows use approximately 1.5 to 2 times more defect inspection steps
  • Automotive flows employ more frequent sampling, both as a percentage of lots and number of wafers per lot
  • Automotive flows use additional sensitivity to capture the smaller defects that may affect reliability

The combined impact of these factors results in the typical automotive fab requiring 50% more process control capacity than their consumer product peers. A closer look reveals exactly how this capacity is deployed.

FIGURE 1 below shows an example of the number of lots between inspection points for both an automotive and a non-automotive process flow in the same fab. As a result of the increased number of inspection steps, if there is a defect excursion, it will be found much more quickly in the automotive flow. Finding the excursion sooner limits the lots at risk: a smaller and more clearly defined population of lots are exposed to the higher defect count, thereby helping serve the automotive traceability requirement. These excursion lots are then quarantined for high-sensitivity inspection of 100% of the wafers to disposition them for release, scrap, or when applicable, a downgrade to a non-automotive application.

FIGURE 1. Example demonstrating the lots at risk between inspection points for an automotive process flow (blue) and a non-automotive (baseline) process blow (pink). The automotive process flow has many more inspection points in the FEOL and therefore fewer lots at risk when a defect excursion does occur.

The additional inspection points in the automotive service package have the added benefit of simplifying the search for the root cause of the excursion by reducing the range of potential sources. Fewer potential sources helps speed effective 8D investigations4 to find and fix the problem. Counterintuitively, the increased number of inspection points also tends to reduce production cycle time due to reduced variability in the line.5

While increasing inspection capacity helps monitor and contain process excursions, there remains risk to automotive IC quality. Because each wafer may take a unique path through the multitude of processing chambers available in the fab, the sum of minor variations and marginalities across hundreds of process steps can create “maverick” wafers. These wafers can easily slip through a control plan that relies heavily on sub-sampling, allowing at-risk die into the supply chain. To address this issue, many automotive fabs are adding high-speed macro defect inspection tools to their fleet to scan more wafers per lot. This significantly improves the probability of catching maverick wafers and preventing them from entering the automotive supply chain.

Newer generation macro defect inspection tools6 can combine the sensitivity and defect capture of many older generation brightfield and darkfield wafer defect inspection tools into a single platform that can operate at nearly 150 wafers per hour, keeping cost of ownership low. In larger design rule 200mm fabs, the additional capacity often reveals multiple low-level excursions that had previously gone undetected, as shown in FIGURE 2.

FIGURE 2. The legacy sample plan of 5 wafers per lot (yellow circles) would have allowed the single maverick wafer excursion (red square) to go undetected. High capacity macro defect inspection tools can stop escapes by reducing undersampling and the associated risks.

In advanced, smaller design rule fabs, macro defect inspection tools lack the needed sensitivity to replace the traditional line monitoring and patterned wafer excursion monitoring roles occupied by broadband plasma and laser scanning wafer defect inspection tools. However, their high capacity has found an important role in augmenting the existing sample plan to find wafer-level signatures that indicate a maverick wafer.

A recent development in automotive control strategies is the use of defect inspection for die-level screening. One such technique, known as Inline Defect Part Average Testing (I-PAT™), uses outlier detection techniques to further enhance the fab’s ability to recognize die that may pass electrical test but become reliability failures later due to latent defects. This method will be discussed in detail in the next installment of this series.

About the authors:

Dr. David W. Price and Jay Rathert are Senior Directors at KLA-Tencor Corp. Dr. Douglas Sutherland is a Principal Scientist at KLA-Tencor Corp.


  1. Price, Sutherland and Rathert, “Process Watch: The (Automotive) Problem With Semiconductors,” Solid State Technology, January 2018.
  2. Price, Sutherland and Rathert, “Process Watch: Baseline Yield Predicts Baseline Reliability,” Solid State Technology, March 2018.
  3. Price, Sutherland, Rathert, McCormack and Saville, “Process Watch: Automotive Defect Sensitivity Requirements,” Solid State Technology, August 2018.
  4. 8D investigations involve a systematic approach to solving problems. https://en.wikipedia.org/wiki/Eight_disciplines_problem_solving
  5. Sutherland and Price, “Process Watch: Process Control and Production Cycle Time,” Solid State Technology, June 2016.
  6. For example, see: https://www.kla-tencor.com/products/chip-manufacturing/defect-inspection-review.html#product-8-series

Richard Dixon, Senior Principal Analyst, Sensors, IHS Markit

Richard Dixon

Sensors are inextricably linked to the future requirements of partially and fully autonomous vehicles. From highly granular dead-reckoning subsystems that rely on industrial-strength gyroscopes for superior navigation to more intelligent and personalized cockpits featuring intuitive human machine interfaces (HMIs) and smart seats, new generations of partially and fully autonomous cars will use sensors to enable dramatically better customer experiences.

Dead reckoning, or, where am I, exactly?

Dead reckoning is the process of calculating one’s current position by using a previously determined position, and advancing that position based upon known speeds over a time slice. As a highly useful process, dead reckoning is the basis for inertial navigation systems in aerospace navigation and missile guidance, not to mention your smartphone.

Today’s best-in-class MEMS gyroscopes can offer 30-50 cm resolution (this is the yaw rate drift) over a distance of 200 m—a typical tunnel length where a GPS signal is lost. For semi-autonomous (L3) or autonomous (L4, L5), the locational accuracy is well below 10 centimeters; that’s an accuracy usually reserved for high-end industrial or aerospace gyroscopes with a raw bias instability ranging from 1°/h and down to 0.01°/h. These heavy-duty gyros command prices from $100s up to $1000s (FIGURE 1).

Figure 1. Current performance levels of different gyroscopes by application and performance measure in terms of bias drift (IHS Markit).

This poses an interesting potential opportunity for both industrial-performance MEMS-based gyroscope sensor-makers, such as Silicon Sensing Systems, Analog Devices, Murata, Epson Toyocom and TDK InvenSense, and for broader-based sensor component-makers such as Bosch, Panasonic, STMicroelectronics, and TDK (InvenSense and Tronics).

While MEMS can master performance, size and low weight, cost remains the challenge. The fail-operational mode requirement for autonomous driving will accommodate higher prices, at least in the beginning, probably in the $100+ range at first, even for the relatively low volumes of self-driving cars anticipated by 2030. Nonetheless, automotive volumes are very attractive compared to industrial applications and offer a lucrative future market for dead-reckoning sensors.

Your cockpit will get smarter

Automakers are banking on the idea that people like to control their own physical environment. Interiors already feature force and pressure sensors that provide more personalized seating experiences and advanced two-stage airbags for improved safety. In some vehicles, automakers are using pairs of MEMS microphones for noise reduction and image or MEMS infrared sensors for detection of driver presence. Eventually, we might see gas sensors that monitor in-cabin CO2 levels, triggering a warning when they detect dangerous levels that could cause drowsiness. These smart sensors would then “tell” the driver to open the window or activate an air-scrubbing system in a more complex solution. While today’s CO2 sensors are still relatively expensive, we may see them designed-in as lower-cost versions come to market.

Future cockpits will need to go beyond such concepts in the lead-up to fully automated driving. Seats could contain sensitive acceleration sensors that measure heart and respiration rates as well as body movement and activity. Other devices could monitor body humidity and temperature.

We need look no further than Murata, a supplier initially targeting hospital beds with a MEMS accelerometer as a replacement for pulse oximeters. That same Murata accelerometer could be placed potentially in a car seat to detect heart rate. It’s not the only way to do this: another sensing approach for heart-rate measurement comprises millimeter wave radiation, a method that can even look through objects such as books and magazines.

Augmenting sensor-based body monitoring, automotive designers will use cameras to fuse information such as gaze direction, rate of blinking and eye closure, head tilt, and seat data with data gathered by sensors to provide valuable information on the driver’s physical condition, awareness and even mood.

Faurecia’s Active Wellness concept—unveiled at the 2016 Paris Motor Show—proves that this technology might be coming sooner than we think. Active Wellnesscollects and analyzes biological data and stores the driver’s behavior and preferences. This prototype provides data to predict driver comfort based on physical condition, time of day, and traveling conditions, as well as car operating modes: L3, L4 or L5. Other features such as event-triggered massage, seat ventilation and even changes in ambient lighting or audio environment are possible (FIGURE 2).

Figure 2. Faurecia’s “cockpit of the future,” announced at CES 2018. (Faurecia).

Meanwhile, there are other commercial expressions of more advanced HMI as well as plenty of prototypes. Visteon’s Horizon cockpit can use voice activation and hand gestures to open and adjust HVAC. Capacitive sensors are already widely used for touch applications, and touchless possibilities range from simple infrared diodes for proximity measurement to sophisticated 3D time-of-flight measurements for gesture control.

Clearly, automotive designers will have a lot more freedom with HMI in the cabin space, providing a level of differentiation that manufacturers think customers will appreciate—and for which they will pay a premium.

Managing sensor proliferation

Researchers are investigating ways to solve the issue of high-functionality vehicles containing myriad sensing inputs, i.e., when we have so many sensing inputs, designers must address wiring complexity and unwanted harness weight. Faurecia, for example, is considering ways to convert wood, aluminum, fabric or plastic into smart surfaces that can be functionalized via touch-sensitive capacitive switches integrated into the surface. These smart surfaces could reduce the explosion of sensing inputs, thereby diminishing wiring complexity. With availability from 2020, Faurecia’s solutions are approaching the market soon.

Beyond functionalized switches, flexible electronics and wireless power sources, and even energy harvesting (to mitigate power sources), could provide some answers. Indeed, recent research has shown that graphene-based Hall-effect devices can be embedded in large-area flexible Kapton films, and eventually integrated into panels. OEMs such as Jaguar Land Rover are interested in such approaches to address the downsides of electronics and sensor proliferation, especially in luxury vehicles. While smart surfaces would represent a big change in sensor packaging and a disruption in current semiconductor processes, they remain a long way from commercial introduction.

By 2030 or thereabouts, fully autonomous cars that detect our mood, vital signs and activity level could well be available. Cabins could signal us to open the window if CO2 levels become dangerous. HVAC systems could increase seat ventilation or turn up the air conditioning (or the heat) based on our body temperature. Feeling too hot or too cold in the cabin could become a thing of the past, at least for the driver, whose comfort level is the most important! We could feasibly feel more comfortable in the car than in our office, our home or at the movies. Perhaps our car will become our office, our entertainment center and our home away from home as we take long road trips with the family, without a single passenger uttering, “Are we there yet?”

Editor’s Note: This was originally published in the SEMI-MEMS & Sensors Industry Group Blog on www.solid-state.com., and published in the October 2018 issue of Solid State Technology.