Georg Pelz

Motor modeling based on physical effect models

Modeling of complex electromechanical systems can be simplified by dividing the mechanical part of the system into basic physical effects. These basic physical effects have been used for microsystem modeling and - as shown in this paper - can also be used in modeling electromechanics of macro scale. The reuse of these models from a library of basic effects in other systems saves time and money. This is illustrated through the example of a hard disk drives electromechanics, i.e. spindle motor and voice coil motor.

Combining models of physical effects for describing complex electromechanical devices

In contrast to classical object-oriented electromechanical modeling, i.e. describing plates, suspensions, drives, etc., this paper proposes to combine models of basic physical effects to object models which in turn can be used for system modeling. This allows one to freely choose which effects are to be considered for some simulations. Moreover, the development of new object models is simplified. The paper shows how to model a wide variety of electromechanical devices with a well-defined library of basic effects.

Bordersearch: an adaptive identification of failure regions

The reliability and safety of modern analog devices, e.g. in automotives, aircraft or consumer electronics, is influenced by many input parameters like supply voltage, ambient temperature or load resistances. In certain regions of this large parameter space, the device exhibits degraded performance or it fails completely. The validation of such a device has to find the regions of the input parameter space in which the device misbehaves. However, with several parameters, it is a complex task to determine these regions, especially if parameters interact. In this paper, we present the Bordersearch algorithm, which combines adaptive testing with a machine learning classifier to efficiently determine the border between passing and failing regions in the parameter space. Furthermore, this method enables sophisticated post-processing analysis, e.g. better visualizations and automatic ranking of the parameters according to their influence. This algorithm scales well to a high-dimensional parameter space and is robust against outliers and fuzzy borders. We show the effectiveness of this method on an automotive electromechanical system with eleven input parameters.

Sequential design of experiments for effective model-based validation of electronic control units

SummaryModel-based verification of automotive electronic control units (ECUs) must ensure compliance with the target requirements in a short time frame. On the other hand, an increasing number of sources of variability (e.g. operating conditions, block parameters) impact system performance. To reduce the overall verification effort, much focus has been put on performance in simulation, and little on how to plan simulation experiments to yield maximum information with minimum number of runs. This paper shows how the classical framework of statistical Design of Experiments can be extended to perform faster and more reliable multivariable sensitivity and worst-case studies. Simulation experiments of a state of the art ECU modelled in SystemC/SystemC-AMS show significant increase in efficiency as compared to traditional approaches.

Design of experiments for effective pre-silicon verification of automotive electronics

The paper presents a method to validate the compliance with value-ranged requirements of heterogeneous electronic systems with variations in a time effective way. Statistical Design of Experiments methodology is applied and adapted to plan, implement and analyze simulations of the system model, to determine key parameters that impact system performance. This is applied on a SystemC/SystemC-AMS model of an automotive Electronic Control Unit.

Mission profile aware robustness assessment of automotive power devices

In this paper we propose to exploit so called Mission Profiles to address increasing requirements on safety and power efficiency for automotive power ICs. These Mission Profiles constrain the required device performance space to valid application scenarios. Mission Profile data can be represented in arbitrary forms like temperature histograms or cumulated drive cycle data. Hence, the derivation of realistic verification scenarios on device level requires the generation of environmental properties as e.g. temperatures, board net conditions or currents. For the assessment of real application robustness we present a methodology to extract finite state machines out of measured vehicle data and integrate them in Mission Profiles. Subsequently Markov processes are derived from these finite state machines in order to automatically generate Mission Profile compliant test scenarios for the design and verification process. As a motivating example we show industry fault cases in which missing application fitness to power transient variations finally results in device failure. Verification results based on lab data are outlined and show the benefits of a fully mission profile driven IC verification flow.

Configurable load emulation using FPGA and power amplifiers for automotive power ICs

In this paper we present a new concept of an application-oriented characterization method for automotive power micro-electronic devices. Automotive power semiconductors are mainly influenced by their real-life application but there is no sufficient method yet to assess device robustness within their application. For that reason we established a first approach to emulate different automotive power loads by running their models in real-time on an FPGA platform while the load current is controlled with a class AB power amplifier. The functionality of this approach is evaluated on the basis of automotive smart high-side switches and incandescent lamp models.

Automotive System Design and Autosar

Automotive system design demands for new solutions for management of complexity and heterogeneity. Heterogeneity in automotive systems does not only cover different physical domains that are combined in a complex system. Heterogeneity in automotive systems also means optimized, proprietary solutions and interfaces. The fact that many tiers are involved, and many configurations have to be maintained is an increasing challenge. This chapter gives an overview of an approach to hide the heterogeneity behind a common and unique interface: AUTOSAR.

Measuring and improving the robustness of automotive smart power microelectronics

Automotive power micro-electronic devices in the past were low pin-count, low complexity devices. Robustness could be assessed by stressing the few operating conditions and by manual analysis of the simple analog circuitry. Nowadays complexity of Automotive Smart Power Devices is driven by the demands for energy efficiency and safety, which adds the need for additional monitoring circuitry, redundancy, power-modes, leading even to complex System-on-chips with embedded uC cores, embedded memory, sensors and other elements. Assessing the application robustness of this type of microelectronic devices goes hand-in-hand with exploring their verification space inside and to certain extends outside of the specification. While there are well established methods for standard functional verification, methods for application oriented robust verification are not yet available. In this paper we present promising directions and first results, to explore and assess device robustness through various pre- and post-Si verification and design exploration strategies, focusing on metamodeling, constrained-random verification and hardware-in-the-loop experiments, for exploration of the operating space.

Simulation-based sensitivity and worst-case analyses of automotive electronics

Simulation-based verification of electronic control units must face demands related to more functionality and less time to verify it. To ensure a reliable system, one must determine how the omnipresent, internal and external variations affect the target response, and find safe bounds for it. The main challenge is to optimally characterize a high number of sources of variation, with a reduced number of simulation runs. The paper conducts more efficient sensitivity and worst-case studies by applying concepts of Design of Experiments: screening to reduce the dimension of the verification space; sequential experiments for sensitivity analysis; gradient-based search for response bounds. The approach is evaluated on simulations of an airbag driver IC and compared with alternative methods.1