Enhancements of MEMS design flow for Automotive and Optoelectronic applications

In the latest years we have been witnesses of a very rapidly and amazing grown of MicroElectroMechanical systems (MEMS) which nowadays represent the outstanding state-of-the art in a wide variety of applications from automotive to commercial, biomedical and optical (MicroOptoElectroMechanicalSystems). The increasing success of MEMS is found in their high miniaturization capability, thus allowing an easy integration with electronic circuits, their low manufacturing costs (that comes directly from low unit pricing and indirectly from cutting service and maintaining costs) and low power consumption. With the always growing interest around MEMS devices the necessity arises for MEMS designers to define a MEMS design flow. Indeed it is widely accepted that in any complex engineering design process, a well defined and documented design flow or procedure is vital. The top-level goal of a MEMS/MOEMS design flow is to enable complex engineering design in the shortest time and with the lowest number of fabrication iterations, preferably only one. These two characteristics are the measures of a good flow, because they translate directly to the industry-desirable reductions of the metrics “time to market” and “costs”. Like most engineering flows, the MEMS design flow begins with the product definition that generally involves a feasibility study and the elaboration of the device specifications. Once the MEMS specifications are set, a Finite Element Method (FEM) model is developed in order to study its physical behaviour and to extract the characteristic device parameters. These latter are used to develop a high level MEMS model which is necessary to the design of the sensor read out electronics. Once the MEMS geometry is completely defined and matches the device specifications, the device layout must be generated, and finally the MEMS sensor is fabricated. In order to have a MEMS sensor working according to specifications at first production run is essential that the MEMS design flow is as close as possible to the optimum design flow. The key factors in the MEMS design flow are the development of a sensor model as close as possible to the real device and the layout realization. This research work addresses these two aspects by developing optimized custom tools (a tool for layout check (LVS) and a tool for parasitic capacitances extraction) and new methodologies (a methodology for post layout simulations) which support the designer during the crucial steps of the design process as well as by presenting the models of two cases studies belonging to leading MEMS applications (a micromirror for laser projection system and a control loop for the shock immunity enhancement in gyroscopes for automotive applications)

Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition

Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrogate models at the low level of the design hierarchy and to handle them in the high-level simulation. In this paper, we develop an efficient ANOVA-based stochastic circuit/MEMS simulator to extract efficiently the surrogate models at the low level. In order to avoid the curse of dimensionality, we employ tensor-train decomposition at the high level to construct the basis functions and Gauss quadrature points. As a demonstration, we verify our algorithm on a stochastic oscillator with four MEMS capacitors and 184 random parameters. This challenging example is simulated efficiently by our simulator at the cost of only 10 minutes in MATLAB on a regular personal computer.Comment: 14 pages (IEEE double column), 11 figure, accepted by IEEE Trans CAD of Integrated Circuits and System

Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques

Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulators rely on Monte Carlo algorithms, which are typically too slow. Therefore, novel fast stochastic simulators are highly desired. This paper first reviews our recently developed stochastic testing simulator that can achieve speedup factors of hundreds to thousands over Monte Carlo. Then, we develop a fast hierarchical stochastic spectral simulator to simulate a complex circuit or system consisting of several blocks. We further present a fast simulation approach based on anchored ANOVA (analysis of variance) for some design problems with many process variations. This approach can reduce the simulation cost and can identify which variation sources have strong impacts on the circuit's performance. The simulation results of some circuit and MEMS examples are reported to show the effectiveness of our simulatorComment: Accepted to IEEE Custom Integrated Circuits Conference in June 2014. arXiv admin note: text overlap with arXiv:1407.302

Circuit modeling of a MEMS varactor including dielectric charging dynamics

Electrical models for MEMS varactors including the effect of dielectric charging dynamics are not available in commercial circuit simulators. In this paper a circuit model using lumped ideal elements available in the Cadence libraries and a basic Verilog-A model, has been implemented. The model has been used to simulate the dielectric charging in function of time and its effects over the MEMS capacitance value.Peer ReviewedPostprint (published version

Materials optimization for magnetic MEMS

By highlighting magnetomechanical effects such as the DeltaE-effect, and developing modeling code that integrates magnetoelasticity with microelectromechanical systems, it is shown that a simple cantilever system can have a sensitivity to mass loading at the attogram level. The requirements on the magnetoelastic materials for such devices are described, and progress towards achieving optimized material is reviewed. The possibility for deployment of such systems in security, healthcare, and bioscience is outline

Workload-Based Configuration of MEMS-Based Storage Devices for Mobile Systems

Because of its small form factor, high capacity, and expected low cost, MEMS-based storage is a suitable storage technology for mobile systems. However, flash memory may outperform MEMS-based storage in terms of performance, and energy-efficiency. The problem is that MEMS-based storage devices have a large number (i.e., thousands) of heads, and to deliver peak performance, all heads must be deployed simultaneously to access each single sector. Since these devices are mechanical and thus some housekeeping information is needed for each head, this results in a huge capacity loss and increases the energy consumption of MEMS-based storage with respect to flash. We solve this problem by proposing new techniques to lay out data in MEMS-based storage devices. Data layouts represent optimizations in a design space spanned by three parameters: the number of active heads, sector parallelism, and sector size. We explore this design space and show that by exploiting knowledge of the expected workload, MEMS-based devices can employ all heads, thus delivering peak performance, while decreasing the energy consumption and compromising only a little on the capacity. Our exploration shows that MEMS-based storage is competitive with flash in most cases, and outperforms flash in a few cases

Design, Fabrication and Characterization of a Piezoelectric Microgenerator Including a Power Management Circuit

We report in this paper the design, fabrication and experimental characterization of a piezoelectric MEMS microgenerator. This device scavenges the energy of ambient mechanical vibrations characterized by frequencies in the range of 1 kHz. This component is made with Aluminum Nitride thin film deposited with a CMOS compatible process. Moreover we analyze two possible solutions for the signal rectification: a discrete doubler-rectifier and a full custom power management circuit. The ASIC developed for this application takes advantage of diodes with very low threshold voltage and therefore allows the conversion of extremely low input voltages corresponding to very weak input accelerations. The volume of the proposed generator is inferior to 1mm3 and the generated powers are in the range of 1$\mu$W. This system is intended to supply power to autonomous wireless sensor nodes.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing