Manuel Harrant

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.

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.

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.

Emulation-based robustness assessment for automotive smart-power ICs

In this paper we present a concept for assessing the robustness of automotive smart power ICs through lab measurements with respect to application variance and parameter spread. Classical compliance to the product specification, where only minimum and maximum values are defined, is not enough to assess device robustness since complex transients of application components cannot be defined within single specification parameters. That is why application fitness becomes a necessary task to reduce device failures, which may occur in the application. One solution would be to enhance traditional lab verification methods with a concept that considers application and parameter spread. This innovative concept is demonstrated on an electronic throttle control application. It has been emulated in real-time, including power amplification and application-relevant parameters. Monte Carlo experiments were carried out within the application space to evaluate the influence of parameter spread on selected system characteristics. Finally, an appropriate metric was used to quantify the robustness of the micro-electronic device within its application.

Monte Carlo based post-silicon verification considering automotive application variances

In this paper we present a fully automated approach to consider device-to-device variances of automotive power applications during post-silicon verification. Due to the high complexity of target applications for automotive smart power microelectronics, it is not sufficient to affirm compliance to their specification. Car manufacturers therefore push for more extensive application robustness beyond classical methods. To cope with this requirement a FPGA platform is used to evaluate physical equations of automotive power application components in real-time together with a dynamic power amplifier to interface the digital FPGA outputs to the analog world. The functionality and the advantage of this approach is evaluated based on several Monte Carlo experiments by using an Advanced Front Lighting system as an example.

Data improvement in lab verification of smart power products using DoE

The paper focuses on treating and characterizing variations that occur in measurements or that are intrinsic in electronic systems. Methods illustrating basic principles of Design of Experiments such as replication and blocking are implemented and used so that valid and objective conclusions are drawn. Replication is used for three purposes: verifying the metamodel adequacy, defining the confidence interval of the measured mean value of the systems response and determining the minimum number of replications that are needed in order to measure the systems response with a given accuracy. Blocking helps with eliminating the systematic error that appears due to the measuring conditions and was used in the screening process. The methods were applied on a lighting control system used in automotive applications.