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TND6233/D
Rev. 1, MAY − 2018

Evaluating Functional Safetyin Automotive Image Sensors© Semiconductor Components Industries, LLC, 2017

May, 2018 − Rev. 1

1

Publication Order Number:
TND6233/D

Evaluating Functional Safety in Automotive
Image Sensors
Abstract
Almost all Advanced Driver Assistance Systems (ADAS) both today and in the foreseeable
future are built primarily on machine vision to drive the decision process. With the rapid
proliferation of ADAS solutions and the introduction of the ISO 26262 safety standard forpassenger vehicles, functional safety considerations for those imaging systems becomesparamount. The manner in which safety measures are implemented and verified can havesignificant impact to the overall system design including cost, reliability, and complexity. This
paper will examine functional safety in the imaging subsystem and its implications to systemdesign.
Introduction
The first rear view cameras appeared in vehicles as early as 1991, primarily as an aid to safe
reversing. In 2004, ON Semiconductor introduced the first CMOS sensor for automotive
applications. In the United States the National Highway Transportation Safety Administration(NHTSA) mandated that by May 2018, all new passenger vehicles are required to include
back-up cameras. Auto makers are now incorporating increasing levels of autonomy to further
improve vehicle safety. As ADAS features like lane keeping assist, adaptive cruise control, andautomated braking for collision avoidance evolve into true autonomy, additional cameras aremaking their way into production vehicles. The primary sensor in almost all ADAS systems is
the image sensor. As ADAS systems progress from assistance to automation, the safe
operation of the vehicle will depend more and more on the reliability of the imaging subsystem.
Underlying this trend is the fact that to ensure a level of safety in ADAS and autonomous
systems, the image sensor becomes a critical component in the system’s overall functionalsafety. With the introduction of ISO 26262, the concept of automotive safety integrity levels has
been defined. ASIL levels range from the lowest, ASIL−A (lowest), to ASIL−D (highest). An ASIL
level is determined by three factors, severity of a failure, the probability of a failure occurring,and the ability for the effect of the failure to be controlled. This paper will explore the issue offunctional safety as it relates to the image sensor as well as to examine failure modes and safety
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2mechanisms that can be implemented to detect, protect, and/or correct image sensor failures.
The key metrics that affect the safety performance of the system include detection, delay,efficiency, and effect.
Faults in semiconductor devices arise from a number of causes including cosmic radiation,
electromigration, early mortality, and a host of other reasons. It is not the objective of this paper
to examine the causes of faults in image sensors, rather it will be to examine the nature of faults,
methods of fault detection i.e. safety mechanisms, and the effectiveness of those mechanisms.
Here we will also discuss some factors that differentiate fault coverage claims andmethodologies.
Functional Safety
To implement functional safety in an ADAS system requires that the system prevent or mitigate
any action or behavior that could cause harm. The assessment of the probability of harm andthe severity of that harm caused by a failure in the system allows system designer to classify
levels of risk the system and to take appropriate measures to minimize the risk.
Often this requires fundamental changes not only in the development process, but also in thecorporate safety culture ranging from organization structure to safety roles/officers and safety
documentation, manuals, and standards. Responsibility for functional safety compliance ina system involves not only the ADAS supplier, but the entire supply chain from OEM to ADAS
supplier to component providers. For robust functional safety, all key safety relevant
components in the system must contribute to the overall functional safety, specifically, thatsafety starts at the source.
In order to minimize the risk caused by a particular failure, the system designers must, ofcourse, identify possible failure modes that could affect the safety of the vehicle and determine
an appropriate action to mitigate the risk. A key part of this process is the identification of allcomponents that could impact the safe operation of the system. For each such component, ananalysis of every possible failure mode must be made to determine whether a given failure modemight contribute to a failure in the system. Once the failure modes have been identified,mechanisms can be implemented to detect, correct, and/or protect the system from, a given
failure.
The specific implementation of safety mechanisms for a given system has a tremendous
impact on the cost, reliability and complexity of the solution, as well as its effectiveness at
mitigating the risks to the system. Various levels of safety mechanisms can be implemented that
range from simple fault detection and reporting, to mechanisms that protect from the occurrenceof faults all the way to actually correcting a fault that has already occurred. A careful andbalanced selection of system components can contribute to a more optimized and efficient
implementation.

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In order to further consider the concept of functional safety, a definition of failure must be
made. In the case of ADAS, we can generally accept that a failure is any condition that causes
the system to make an incorrect or even less than optimal decision. Examples of undesired
decisions include late braking, over-steering, false object identification, or unintended
acceleration.
Image Sensors and the ADAS Application
Image sensors are a core component of an ADAS system and the primary source of all visionsystem data. They provide the raw data which the rest of the system uses to analyze theenvironment and then make operational decisions in the vehicle. In effect, image sensors are
the eyes of the autonomous vehicle. Other sensors like radar and lidar may also be used, but
the primary source of data are the image sensors. In addition to the sensors, other components
in the ADAS system include components that perform the functions of image processing,analysis and decision making.
As stated previously, the number of image sensors in a typical ADAS system is rapidly
growing. From a single forward looking camera, to full surround view systems, the number ofcameras in a vehicle can be anywhere from one (1) to over ten (10). The effect of failures in thesensor depend on the nature of the failure and can range from insignificant to critical. The ability
for a system to detect, protect and correct individual failures in the image sensor have significant
ramifications to the overall safety and reliability of the system.
At its core, a CMOS image sensor is a rectangular array of photo-sensitive pixels organized
in rows and columns. These pixels convert the incident light into voltage or current witha per-pixel analog circuit. The current/voltage is then converted into digital values, typically ina row-by-row order. Additional digital logic enables the data to be stored, processed, andtransmitted to other devices in the system for subsequent processing and analysis.

Power

Trigger
Two-wire
SerialInterface

Active Pixel Sensor
(APS)
Array

Temperature
SensorOTPMTiming and Control(Sequencer)

MemoryAuto Exposureand Stats Engine

Pixel Data Path
(Signal Processing)

Analog Processing and
A/D Conversion

PLL

External
Clock

SerialOutputParallel
OutputFlash

Control Registers

Figure 1. Block Diagram of a Typical Image Sensor

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The data captured by the image sensor in an ADAS application is typically used by the system
to make decisions that affect the operation of the vehicle. As ADAS systems have increased incomplexity, these decisions have advanced from generating simple audible and visual
warnings, to much more complex decisions including braking, acceleration and steering, andin the future will progress to completely autonomous driving. These advances in autonomousand semi-autonomous vehicles places increasing reliance on the image sensor and it safe
operation.
Failures in Imaging Applications
A very conservative view of a failure in an image sensor would be to define an unsafe fault as
any output that differs from a “fault-free” model or known-good device output as shown by the
diagram below. At a granular level, this would imply that errors even at the pixel level could
constitute a failure. At higher levels, row, column and frame errors could also constitute a failure.
Implied are any problems in the internal operation of the device, either analog or digital, that
could manifest themselves as pixel, row, column, or frame errors. Finally, errors in the physicaltransmission of data from the sensor to the rest of the system present another potential cause
for failure. Due to the dynamic nature of video, faults can be both static, i.e. permanent or fixed,
and dynamic, both spatially and temporally.
Sensor Modelwith Fault

Imagewith Fault
Difference

Sensor Model
No Fault

Reference
Image

Yes

Unsafe Fault

No
Safe Fault

Figure 2. Flowchart to Determine Safe vs. Unsafe Faults
Taking this conservative definition of failure in an image sensor, the challenge to the systemdesign is the detection of the presence of a failure. Additionally, the system may take measures
to protect against the occurrence of a system failure or to correct or take corrective action in thepresence of an individual sensor error. To better understand the implications to system levelsafety mechanisms, we will explore several possible failure modes of an image sensor andmechanisms to detect those failures.
Faults that affect individual pixels may appear to have minimal impact to an ADAS system.
However, considering the fact that many of the most advanced object detection algorithms candetect objects in the image of less than 10 pixels × 10 pixels, individual pixel errors, and certainly
error clusters, might affect an object identification algorithm. Also, failures that contribute to pixel
errors are likely to affect some proportion of pixels across the array. Since a pixel output iswww.onsemi.com
5converted to a digital value representing the intensity of light at a given position, a failure canbe considered to be any error or corruption that causes any incorrect value, whether static ordynamic. Factors including power delivery, device defects, excessive noise or even ambient
radiation could cause errors.

Figure 3. Example of a Fault in the Clock System
Due to the array nature of image sensors, logic associated with the row/column structure of
the array may also contribute to device faults. Missing or duplicated rows and/or columns canresult in loss of information or incorrect representation of the scene. Obvious errors likea repeating frame in a rear-view system could lead to catastrophic consequences byautonomous, semi-autonomous, and human driving. Even if all elements of the image frame,
pixel, row/column and frame data are error-free, transmission errors can cause corruption of thedata before it reaches the intended receiver device. These transmission errors can be causedby any number of natural phenomena that themselves are undetectable by the system.
The failures described here are general categories of failures each comprising hundreds ofindividual failure modes. In fact, there are literally thousands of individual failures in an image
sensor that could lead to incorrect data being received by downstream devices. Needless to saythat decisions based on the incorrect data could lead to a safety risk. Ultimately, the system must
be able to identify and detect the occurrence of these failures in order to take risk mitigating
actions.
Challenges in Fault Detection
Detection of faults in an image sensor is a non-trivial exercise. The nature and complexity of
the image sensor results in a staggering number of failure modes that could occur. The mix ofboth analog and digital circuitry further aggravates the problem.
The pixel structure and associated charge transfer and readout circuits are analog in nature.
Faults associated with analog circuits have different behavior than those in digital circuits.
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6During operation, a pixel may suffer from a fault similar to a digital stuck-at fault (which occurs
when a logic node becomes “stuck-at” a high or low value). Detecting a fault like a stuck pixel
may appear to be trivially done on a host processor. But as sensor resolutions increase to
8 Mpixels and above, checking every pixel for any of several fault conditions on every frame fora given window of time can begin to consume a significant number of processor cycles andmemory. Detecting some types of pixel faults, for example noise outside the specified limits,
may not even be achievable at the system level. Detecting faults in the analog-to-digitalconversion stage, faults that include missing codes, noise, and non-linearities, may also be
prohibitive or impossible to perform on a host processor or at the system level.
Scene

Reference Image“Fault” Image

Figure 4. Example of a Fault in the Analog Pipeline
In addition to analog faults at the pixel level, the system must contend with digital faults at the
pixel level as well. If pixel data is affected by digital errors that cause bits to be shifted, higher
level processing may be unable to even detect these errors. Similarly, while some types of color
space errors may be easily visible to the human eye, computing devices may be unable to detectsuch faults. Systemic faults in the image processing and transmission pipeline can cause widely
ranging error behaviors that may or may not be detectable by the system.

Figure 5. Example of a Color Error due to an Image Pipeline Fault

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7Spatial errors such as row or column addressing errors that result in repeated rows could be
detected at the system level but at a cost of CPU cycles and memory. The system has no
guarantee that the sensor is even sending the rows and columns in the correct order andvirtually no way to verify it. There may be generic methods to determine that consecutive images
are similar to prior images, but these may only indicate gross failures in the sensor. More subtle
failures are still beyond the scope of the system to detect. Even in cases where detection at thesystem level is possible, accounting for the vast number of failure modes that are possible andperforming the analysis required to detect them would be prohibitive in terms of compute power
as well as being incomplete in coverage.
The last three failure modes we could consider are probably more commonly encountered inother digital circuits. The first is ensuring that the data transmitted by the sensor has not beencorrupted prior to being received as the data may have to traverse long and noisy transmission
media. The second is ensuring that the memories and registers within the sensor are functional
and that faults can be detected and/or corrected. The third is a failure in the internal logic or statemachines of the sensor.

Figure 6. Example of a Fault in the Row Addressing Logic
The first may be solved by using transmitters and receivers with built in error checking and/or
error correcting coding. At the very least this adds cost to the system. The second can be solvedby the system by periodically checking the register and memory contents of the sensor, but thisconsumes system resources. The third could cause issues ranging from catastrophic corruptionof image data to more insidious changes that gradually corrupt frame data of the course of many
frames. The former type could be easily identified while the latter may be completely invisible
to any system level checking.

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8FaultFault Detected

State

Hazardous
Event

NormalOperation

SensorFrame
Time

SystemDetection
Time

ReactionTime

Safe State

Time

Fault

Fault Detected

State

Hazardous
Event

NormalOperation

Sensor
Detection
Time

Reaction Time

Safe StateTime

Figure 7. System-based Fault Detection Time vs. Sensor-based Fault
Detection Time
Another factor to consider is the delay between the occurrence of the fault, and its detection.
Commonly referred to as Fault Detection Time Interval (FDTI), the detection delay has
a significant impact on the overall time between the occurrence of the fault and the transitionof the system into a safe state before a hazardous event occurs, or Fault Tolerant Time Interval(FTTI), as shown in the illustration above. In the case where the system is required to perform
some or all of the fault detection, the overall FDTI includes the time for the sensor to transmit
the data to the next stage of the system, as well as the time required for the system to receive,
analyze and finally detect the presence of a fault.
In addition to the sheer magnitude of faults to be detected, the potential for undetectable faults
at the system level, the additional delay and incremental detection time, the significant computeand memory requirements and the demand to perform these diagnostics in real time, is thehidden cost. The cost to implement fault detection at the system level arises from several
contributors. First the additional cost of higher performance CPUs, GPUs and memories, butalso the additional development costs incurred to develop the diagnostic algorithms to detect
the faults. Increased power consumption and its related thermal dissipation also factor into thecost equation. All of these costs still result in a diagnostic coverage that may have significant
weaknesses.
Ultimately, any system level fault detection mechanisms divert resources away from thesystems’ fundamental goal. This diversion of resources from the systems’ primary function adds
cost, reduces functionality, or increases complexity impacting the effectiveness,
responsiveness, and efficiency of the system. By using sensors with integrated safetymechanisms, system designers can more effectively and efficiently focus resources towards theprimary goal.

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9Advantages of Sensor-based Functional Safety
Sensors today offer a range of test capabilities integrated into the device. Some image
sensors provide the ability to transmit a defined test frame. Performing a CRC check on the data
could indicate a possible fault in transmission. This is a good first step towards fault detection,but often the test frame does not exercise any significant portion of the actual image capture
pipeline, especially the analog portions. This type of check typically only indicates faults in thetransmission data path and not in the sensor itself. Additionally, the faults caught by this method
tend to be static failures. Finally, the generation of a test frame also takes the sensor, andtherefore the entire system, offline for a finite period of time. All of these drawbacks point to theneed for a real time method of detecting possible faults at the pixel level of the image sensor.
When considering image sensors for an ADAS or autonomous vehicle system, analog fault
coverage should be a serious consideration. More advanced sensors offer significant functionalsafety mechanisms that provide diagnostics of the analog portion of the sensor, which in mostmodern sensors occupy more than 50% of the total circuit area. A high level of analog diagnostic
coverage is essential to robust image sensor functional safety. A simple metric to differentiate
sensors can be the number of analog safety mechanisms supported by the sensor. While certain
analog safety mechanisms may require some additional computational, a key factor will be theamount of additional processing required to detect the fault. More advanced safety mechanisms
will require less computation, often limited to bounds checking, while less sophisticatedmechanisms will require more elaborate, compute intensive processing.
Another step towards safety is the inclusion of a frame counter within the sensor. This allows
the system to detect when capture has failed for some reason. Counting pixels and lines canprovide even better fault coverage by detecting that the sensor is transmitting the correct
number of rows and columns per frame. This may capture dynamic failures, but the detectionof missing columns or rows indicates that the fault is fairly severe and renders the frame
unusable.
These failure modes produce errors that vary in nature from randomly distributed errors to
repetitive or fixed errors that require varying levels of computing power and memory to detect.
Individually, detection of a given error could be efficiently performed by any ADAS systemprocessor. While detecting any given type of fault may be possible with some backend
processing, detecting every possible type of fault in every frame becomes a monumental taskeven for the highest performance processors available today. Having on-sensor functionalsafety mechanisms that perform the bulk of the fault detection could reduce this monumental
computing demand to the simple checking of status or health indicator bits or registers that
consume virtually no significant system resources.
In addition to significantly reducing computational demands on the system, sensor based
diagnostic coverage can also significantly reduce the Fault Detection Time Interval. By signaling
the fault in the image stream, the Detection Time can be reduced from T1 + T2, to only T1 as
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shown in the drawing below. With sensors typically running at 60 frames per second (fps),
a sensor with a detection time of one frame can reduce the FDTI to about 16 ms. This gives thesystem additional margin and increases the available Fault Reaction Time Interval.
Environment
Mapping

Sensors
T1

Decision
EngineControlT2

Sensors

Radar

LiDAR

Figure 8. Autonomous Driving System Model with Fault Detection Times
The most advanced functional safety devices today take into consideration the wide range offailure modes that could occur in an image sensor and offer three key advantages. First, theability to the lowest latency from failure to notification. Second, the ability to provide safety
notification in real-time, without affecting the operation, quality, or performance of the sensor.
And finally, to offer the highest fault coverage at the lowest computation and cost.
Fault Coverage and Verification
Many image sensor vendors make bold claims of high fault coverage including ASIL−B and
ASIL−C support, but how can Tier 1 manufacturers and OEMs verify these claims? Anotherimportant factor to consider is the ability to develop a system with higher fault coverage
incorporating a sensor with a lower ASIL level, i.e. ASIL decomposition. Here we will examine
how diagnostic coverage is typically determined and address the questions of verification and
ASIL decomposition.
Typically, diagnostic coverage is based on guidelines given in ISO 26262−5 Annex D.
However, the same source is careful to identify that: “The assignment of the faults and theircorresponding safety mechanisms to diagnostic coverage levels can vary from that listed inTable D.1”.
However, many sensor manufacturers quote these numbers solely based on the type of test
implemented, with little or no consideration of the details of the implementation or any of theother variations presented with the above quoted clause in ISO26262−5 Clause D.1. This
usually results in artificially high diagnostic coverage estimates and of course, to the benefit ofwww.onsemi.com
11the sensor vendor, an equally artificially high ASIL rating. This begs the question of how toaccurately determine diagnostic coverage of safety mechanisms.
The best way to determine diagnostic coverage is through actual fault injection to determine
if a given fault is detected by a safety mechanism. However, with the number of gates in a typicalimage sensor being in excess of 1.5 million, exhaustive fault injection is practically infeasible.
In addition, automotive image sensors today can contain over 8 million pixels in addition to otheranalog circuitry. To address this, statistical methods can be employed that enable the calculationof diagnostic coverage within a given margin of error. Statistical fault injection can be effectively
used to achieve margins of error of less than 5%. This gives the ability to accurately calculatediagnostic coverage to within a few percent.
When considering the overall safety of an autonomous vehicle, understanding the diagnostic
coverage of an image sensor to a high level of accuracy is vital. Having an image sensor whose
diagnostic coverage estimation based on recommendations and guidelines creates a highdegree of uncertainty when performing the safety analysis of the overall system. Conversely,
having an image sensor whose diagnostic coverage is known to be accurate to within a few
percent gives high confidence in the overall safety of the autonomous system. Documents suchas the FMEDA (Failure Modes, Effects, and Diagnostics Analysis) can give a clear picture of
how the safety mechanisms are tested and how diagnostic coverage is calculated.
Conclusion
Clearly, without direct support for detection, protection, and correction of failures within theimage sensor itself, the ability for an ADAS system to achieve a desired ASIL levels is severelycompromised. Conversely, the ASIL level of the ADAS system can be greatly improved withsignificant ASIL support directly in the image sensor.
As driving automation increases, the required safety level of the ADAS subsystem will
increase. Even today many ADAS systems struggle to meet ASIL−B compliance. In the nearterm, the number of systems that are required to meet ASIL−B compliance will increase
dramatically. Future ADAS systems will be required to meet even more rigorous ASIL−C and
ASIL−D compliance. By ASIL decomposition, an ASIL−C ADAS solution could be built only on
top of ASIL−B image sensor. In fact, it may even be possible to build an ASIL−D ADAS systemusing an ASIL−B image sensor through complimentary safety goals. With designs today
targeting car models many years in the future, incorporating image sensors with robust ASIL
safety features and mechanisms on-sensor greatly improves the ability of the system to meethigher and higher levels of ASIL compliance while reducing system cost and complexity. Finally,using image sensors with highly accurate calculation of diagnostic coverage through statisticalfault injection greatly improves confidence in the overall safety of autonomous vehicle systems.

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ON Semiconductor andare trademarks of Semiconductor Components Industries, LLC dba ON Semiconductor or its subsidiaries in the United States and/or other countries.
ON Semiconductor owns the rights to a number of patents, trademarks, copyrights, trade secrets, and other intellectual property. A listing of ON Semiconductor’s product/patent
coverage may be accessed at www.onsemi.com/site/pdf/Patent−Marking.pdf. ON Semiconductor reserves the right to make changes without further notice to any products herein.
ON Semiconductor makes no warranty, representation or guarantee regarding the suitability of its products for any particular purpose, nor does ON Semiconductor assume any liability
arising out of the application or use of any product or circuit, and specifically disclaims any and all liability, including without limitation special, consequential or incidental damages.
Buyer is responsible for its products and applications using ON Semiconductor products, including compliance with all laws, regulations and safety requirements or standards,
regardless of any support or applications information provided by ON Semiconductor. “Typical” parameters which may be provided in ON Semiconductor data sheets and/or
specifications can and do vary in different applications and actual performance may vary over time. All operating parameters, including “Typicals” must be validated for each customer
application by customer’s technical experts. ON Semiconductor does not convey any license under its patent rights nor the rights of others. ON Semiconductor products are notdesigned, intended, or authorized for use as a critical component in life support systems or any FDA Class 3 medical devices or medical devices with a same or similar classification
in a foreign jurisdiction or any devices intended for implantation in the human body. Should Buyer purchase or use ON Semiconductor products for any such unintended or unauthorized
application, Buyer shall indemnify and hold ON Semiconductor and its officers, employees, subsidiaries, affiliates, and distributors harmless against all claims, costs, damages, andexpenses, and reasonable attorney fees arising out of, directly or indirectly, any claim of personal injury or death associated with such unintended or unauthorized use, even if suchclaim alleges that ON Semiconductor was negligent regarding the design or manufacture of the part. ON Semiconductor is an Equal Opportunity/Affirmative Action Employer. This
literature is subject to all applicable copyright laws and is not for resale in any manner.

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