Electronics and in particular, sensor technology, have made a decisive contribution to advances in the automobile as a means of accessing vehicles and interacting with its surroundings. The Magnetoresistive Effect supports a variety of sensor applications in automobiles. Since their first use in thin-film technology 30 years ago, MR-sensors have constantly captured new fields of application in magnetic field measurement.
An integral part of today's automobile, sensor controlled system serve primarily for contactless detection of mechanical variables. Sensors of this kind are usually implemented either by Hall elements or based on the Anisotropic Magnetoresistive (AMR) Effect. The AMR effect, discovered by Lord Kelvin in 1857, has proven especially suitable for detecting magnetic fields. Compared to solutions using the Hall Effect, AMR sensors possess a number of advantages, such as less jitter and higher sensitivity. Both can contribute in equal measure to higher accuracy or reduction of overall system costs.
Magnetoresistive sensors in automobiles are used to determine angle and speed, where the magnetic field indicates the motion or position of a mechanical system. Data of this kind is needed by anti-skid systems and engine and transmission controls. In-depth analysis using simulation is essential before an overall system is implemented. As all components influence the way in which a system responds, a great deal of importance lies in the simulation of an overall system, especially during the planning stages and understanding system requirements. Here we look at the modeling and simulation of an overall system in the case of a new speed sensor.
Modern sensor systems consist essentially of two components — an elementary sensor and a signal-processing ASIC (Fig. 1). The Anisotropic Magneto Resistive (AMR) effect occurs in ferrous materials like permalloy. Permalloy is an alloy of 81% nickel and 19% iron and has been used as a sensing material since the early part of the 20th century. Fig. 2. represents a thin film of permalloy with current flowing through it. When an external magnetic field is applied to permalloy, the change in its resistance is proportional to the square of the sine of the angle α. Ferromagnetic materials, like Permalloy, have magnetization, which is a vector quantity defined at each point in the material. It is the rotation of this magnetization vector from the direction of the current flow due to an external magnetic field which produces the change in resistance. The magnitude of the change in resistance depends on the properties of the permalloy. Permalloy's properties cause it to change resistance by 2%-3% in the presence of a magnetic field. The elementary sensor of all sensor systems from NXP is implemented by such a permalloy.
The setup to determine speed consists of two components: an encoder wheel and the sensor system. The encoder wheel can be either active or passive. An active wheel is magnetized and an MR sensor detects the change between north and south poles. In the case of passive wheels, the magnetization is replaced by a tooth structure. Passive encoder wheels usually have very small tolerances. When the sensor symmetrically faces a tooth or a gap between two teeth of a passive wheel, it produces no deflection of magnetization vector of AMR elements. Neglecting external noise fields, the output signal achieves a value of zero. If the sensor head is in front of a tooth edge, the magnetic input signal becomes extremal. The result as a function of the type of change tooth/gap or gap/tooth, to a good approximation, is the minimum or maximum of a sinusoidal magnetic input signal.
To determine the speed, the magnetic input signal is encoded into an electrical pulse sequence and transmitted by a 7/14 mA protocol. A comparator can be used to generate the pulse sequence. Hysteresis is usually added to the comparator circuit to eliminate the influence of lower levels of noise. Marked fluctuations in the gap between the sensor head and encoder wheel lead to fluctuations in the amplitude of the magnetic input signal.
Magnetic offset also endangers the working of the system. A noise field can boost or reduce the actual measured signal to such an extent that only one or neither of the thresholds of the Schmitt trigger is exceeded or underrun. Very high speeds of passive wheels can create eddy currents in the wheel that in turn produce a magnetic noise field. The resulting offset is a risk to operating reliability.
To eliminate the influence of this noise on the output signal, a signal-processing ASIC is housed in another package. The latter also holds a line driver to provide the supply voltage for signal processing and a high-volt interface (Fig.1). Fig. 4 illustrates the signal-processing architecture. The central elements to eliminate malfunctions are an adjustable amplifier, offset cancellation, and smart comparator. The adjustable amplifier, depending on the distance between the sensor and encoder wheel, can match the signal level. For offset cancellation there is a control system that eliminates offsets and also maintains the 0 Hz capability of the system which helps to detect a standing encoder wheel. The thresholds of the smart comparator are variable and can be set so that hysteresis is between 20 and 45% of the signal amplitude. This ensures sufficient noise suppression. The system described above was developed and validated with simulation support. The following outlines the development and also shows how the model can be used for advantage in the design-in.
To develop the sensor system, it is first necessary to gain an overview of the expected magnetic input signals. The starting point for this is specifications of the encoder wheel, the permanent magnet on the sensor head, the expected dimensions and tolerances. FEM simulations by ANSYS were conducted to determine the magnetic fields. The positions of the encoder wheel, sensor element, and the magnet determines the field strength. Fig. 6 shows the magnetic input signal on the sensor bridge as a function of distance. In addition to the distance, deviations in position also cause the amplitude to reduce. Based on FEM simulation, mechanical specifications can thus be converted into expected magnetic variables. FEM simulation is also suitable for estimating their influence (Fig. 7), and the results can be translated directly into admissible position tolerances.
Determining the magnetic fields is followed by simulation of the sensor system. The change in resistance of the AMR elements is a direct result of the AMR effect. The results of field simulation lead to a change in resistance that represents the input signal of the signal processing. Simulink was used to model the analog front-end. Each Simulink block corresponds to a component of the analog signal processing, e.g., an amplifier or filter. HDL design simulates the digitally implemented functionality, and is ready in final form after completing product development. Simulation of the overall system is consequently a co-simulation of the behavioral model of the analog components by Simulink and the HDL design by ModelSim (Fig. 8). A Simulink reference model can gradually be replaced in a co-simulation by ready implemented Verilog code in ModelSim, and the HDL design can thus be validated piece by piece. The process can be continued until the entire digital part is implemented in Verilog, while the analog system parts remain as a Simulink model. This tool combination has also proved useful for evaluation of the IC.
Modeling of this sort makes it possible to analyze the behavior of the system as a function of the input signals. The first diagram in Fig. 9 shows a magnetic input signal produced by varying the distance between the sensor and encoder wheel. This signal is the result of finite element simulation, which can be converted by the AMR effect into an electrical output signal of the bridge. The middle diagram is the result of analog signal processing. The bottom diagram shows the output signal. Fig. 10 shows examples of signal patterns in ModelSim.
Control of the simulations by MATLAB and combination with additional simulators create extra options. Simulations can be automated and there is the possibility of using extensive algorithms for signal evaluation in MATLAB. Through FEM simulators such as ANSYS, it is possible to extend the simulated system components as far as the MR sensor head and the associated encoder. Fig. 11 illustrates the whole tool chain for this purpose.
The authors are employees of NXP Semiconductors Germany GmbH, Automotive Innovation Center (AIC), Hamburg.
Fig. 1. AMR sensor systems consist of two packages
Fig. 2. Anisotropic magnetoresistive effect
Fig. 3. Configuration of AMR elements on die
Fig. 4. Signal-processing concept of modern speed sensor
Fig. 5. The mesh — starting point for finite element simulation of magnetic fields
Fig.6. Simulation of magnetic input signal as a function of distance between sensor head and encoder wheel
Fig. 7. Field calculation to determine admissible position tolerances
Fig. 8. Co-simulation of analog front-end and digital block
Fig. 9. Simulation result: electrical output signal vs magnetic input signal
Fig. 10. Simulation of digital system components
Fig. 11. The complete simulation chain