E NVIRONMENTAL S TRESS S CREENING
6.4. Case study A: Inertial Measurement Units
Inertial Measurement Units (IMUs) represent an essential part of monitoring, diagnostic, and/or controlling system in many different application fields, like navigation and transportation, automotive and self-driving vehicles, Unmanned Aerial Vehicles (UAVs) and aerospace devices, cellular phones, human motion, robotics, and many other contexts (see for instance but not only [24], [26], [202]–
[206]).
Based on the complexity, costs, size and weight requirements of the specific application, Inertial Measurement Units could integrate all the following sensors, or only a subset of those [29], [207]:
• A triaxial accelerometer used to measure the linear acceleration toward the three axes.
• A triaxial gyroscope used to measure the angular rate toward the three axes.
• A triaxial magnetometer used to measure the static magnetic field toward the three axes.
Several technologies of IMU are available in the market. Micro-Electro-Mechanical Systems (MEMS) devices are a practical, low-cost, and low-power solution that allows to ensure high accuracy and stable performances within small easy-integrated chip. Consequently, nowadays, MEMS-based IMUs are dominating the inertial platform market in every field of application [23], [32], [208]–[210]. For more information about MEMS-based IMU see section 2.3.
6.4.1. Research motivations
The expected performance of such systems is provided in the related datasheets by microelectronic manufacturers which, generally, consider simplified operating conditions that are not well representative of the actual way of such devices operating. Indeed, typical information that can be found in datasheets deals with selectable ranges for measuring linear acceleration, angle rate, and static magnetic field, as well as the related sensitivity (to each detected quantity) and the temperature operating range. Nevertheless, the influence of the actual operating conditions on the dynamical metrological performances and on the system reliability is not sufficiently dealt with.
Recent literature extensively focuses on MEMS-based IMUs design and calibration (see, for instance, but not only [24], [25], [31], [32], [202], [204]).
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Quite the opposite, the characterization of IMUs under a real operating context well-representative of actual scenario is not adequately considered.
Despite this lack, a context-awareness reliability analysis and metrological performance analyses of MEMS-based IMU under the actual operating conditions are crucial for many application fields. Relevant temperature variation, high humidity level, significant vibration stresses, mechanical shocks are only a few lists of environmental factors that profoundly affect the performances of microelectronic devices [211]–[213].
As an example, in the automotive context, in navigation and industrial environments, IMUs mounted on terrestrial vehicles are continuously interested in mechanical stresses as random vibrations, so it is expected that the effects of such vibrations could generally affect both the metrological performance, the reliability and the time to failure [126], [214].
Another example of such criticalities arises when commercial IMUs are mounted in UAVs (and more generally in aeronautical applications). Varying for instance the altitude of the flight, the temperature and humidity of the external air could remarkably change, leading to drifts or even failure of the measurement units [30], [210].
Furthermore, a context-awareness analysis is the most suitable one to characterize the performances of either software or algorithms that are embedded in microcontrollers. In fact, the real operating conditions could activate some hardware failure mechanisms that in nominal conditions are generally neglected, as well as could lead to software malfunctions, parameters drift, increase of the convergence time, data misinterpretation, and many other issues [215]. As for the low-cost IMUs, which are widely employed in popular applications as low-cost UAV and automotive, they are generally characterized by low Output Data Rates (typically close to 100 Hz).
On the other hand, many international testing standards agreed that road vehicles experienced vibration up to hundreds of Hz. Consequently, it is fundamental to investigate the performances of such devices in a context-awareness scenario to identify possible unexpected behavior in the presence of a high-frequency stimulus. Despite this, testing the performance of positioning algorithms operating under vibration stimuli comparable to the real vibration (impressed for instance, by the wind or by motor propulsion) is another significant aspect not adequately described in recent literature.
Furthermore, there is another missing point in recent literature. Currently, there are not international standards specifically designed for the environmental test of IMUs, as well as customized standards for MEMS devices are not yet available.
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6.4.2. Aim of the test procedures
Trying to fill all the above-mentioned gaps, this work proposes a customized test plan and a measurement setup in order to characterize the effects of the actual operating conditions (in terms of temperature and vibration) on a set of widely-used commercial low-cost MEMS-based IMUs for automotive and UAVs applications.
The operating performances of both automotive and low-cost commercial UAV applications were considered to develop a test that is well-representative in terms of both these contexts.
The proposed test plan allows to carry out different kinds of analysis at the same time focusing on many goals strictly related to each other:
• Screening of the weak population by a reliability point of view. The proposed test plan can be contextualized within the context of ESS-RSS test procedures. Thus, the investigation of early failure due to infant mortality can be performed. This will allow to significantly decrease the failure rate of the device in the first phase of its lifecycle, avoiding unexpected early failures.
• Characterization of the IMU’s metrological performances under harsh environment. The analysis of the raw data outcoming for the IMUs during the test allows to estimate miscalibration issue, loss of accuracy and stability, cross-axis sensitivity, spurious response ratio, etc.
• Characterize the performances of different positioning algorithms under temperature and vibration conditions. Two different well-known filtering algorithms for positioning were tested basing on the data acquired by the IMUs during the tests. More in detail, the considered filtering algorithms are based on the Complementary filter and on the Attitude and Heading Reference Systems (AHRS) Kalman filter with the aims of analyzing and comparing two kinds of opposite approaches:
the former based on suitable high/low pass filters, and the latter based on the capability of prediction given by the Kalman filter.
• Analysis of the IMU frequency response in presence of an additive high-frequency white gaussian noise, typical of the automotive and aerospace contexts.
• Verification of the MEMS-based IMU performances in order to ensure that the system will work properly when it is subjected to vibration and temperature stresses (well-representative of low-cost automotive or UAVs applications).
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6.4.3. Failure analysis of MEMS-based IMU
The environmental conditions remarkably affect the metrological and electrical performances as well as the component reliability of microelectronic devices.
For instance, MEMS-based IMUs developed to work on automotive and UAV applications are subject to temperature excursion and vibration conditions that could cause fatigue and fracture on the devices, as well as stiction of the mechanical parts, creep and plastic deformation, short and open circuit, etc.
[216]–[219].
A preliminary functional failure analysis is required prior the draft of the test plan to understand every possible failure mechanism that could lead to faults and malfunctions of the whole inertial module. In this work, the failure analysis focuses on both mechanical and electrical physical domains in order to cover all the failure mechanisms typical of MEMS technology.
According to many works in recent literature (see for instance [218], [220]–
[227]), the typical failure mechanisms of MEMS sensors are detailed included in TABLEVI.II.
The table also provides a list of one or more failure causes and one or more acceleration factors for each one of the failure mechanisms. The acceleration factors are extremely helpful parameters useful to understand which environmental condition influences most the component reliability and its performances [228], [229].
Vibrations, and more generally all mechanical shocks, influence almost all the possible failure mechanisms of the MEMS sensors, as highlighted using blue bold character in TABLEVI.II.
Quite similarly, also temperature is a critical influence factors able to trigger many failure mechanisms of MEMS devices, as emphasized using red bold character in TABLEVI.II. As a matter of fact, temperature stress could be useful to investigate the ability of soldering to endure high and low temperatures, as well as it is useful to highlight electrical and/or physical drifts in the parameters of the microelectronic device. These parameter drifts produce effects on both system performances and reliability.
As a conclusion, almost all the identified failure mechanisms can be quickly investigated using thermal and vibration tests. For this reason, this work proposes an ESS-based test plan based on two different accelerating factors:
temperature and vibration. Different test procedures based on these two stress sources have been developed in order to fulfil all the test objectives presented in section 6.4.2.
153 TABLE VI.II
FAILURE ANALYSIS OF MEMS SENSORS INCLUDING FAILURE MECHANISMS, ROOT CAUSES AND ACCELERATION FACTORS.
FAILURE
MECHANISM ROOT CAUSES PHYSICAL DOMAIN
ACCELERATION FACTORS
Creep and plastic deformation
Intrinsic stress Applied stress Thermal stress
Mechanical
Temperature Applied strain Vibration Fracture due to
mechanical shocks and vibrations
Overload Fatigue Shock
Stress corrosion
Mechanical
Acceleration Frequency (at resonance) Vibration
Wear
Adhesive Abrasive Corrosion Surface fatigue
Mechanical
Mechanical shock Speed
Temperature Environment
Contamination
Intrinsic
Fabrication-Induced User Environments
Mechanical Environment Temperature
Stiction
Capillary force Van der Walls force Electrical static force Residual stress Chemical bonding
Mechanical Electrical
Humidity
Mechanical shocks Vibrations Voltage
Short/open circuit
Dielectric material degradation High electric field
Electromigration Ohmic contact
Electrical
Electric field Temperature Humidity
Dielectric charging
Dielectric material degradation
High electric field Electrical
Electric field Temperature Radiation Humidity Electrostatic
discharge (ESD)
Static electricity
Electrostatic induction Electrical Charged devices Charged Environment
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