System integration design in MEMS - A case study of micromachined load cell

One of the critical issues in large scale commercial exploitation of MEMS technology is its system integration. In MEMS, a system design approach requires integration of varied and disparate subsystems with one of a kind interface. The physical scales as well as the magnitude of signals of various subsystems vary widely. Known and proven integration techniques often lead to considerable loss in advantages the tiny MEMS sensors have to offer. Therefore, it becomes imperative to think of the entire system at the outset, at least in terms of the concept design. Such design entails various aspects of the system ranging from selection of material, transduction mechanism, structural configuration, interface electronics, and packaging. One way of handling this problem is the system-in-package approach that uses optimized technology for each function using the concurrent hybrid engineering approach. The main strength of this design approach is the fast time to prototype development. In the present work, we pursue this approach for a MEMS load cell to complete the process of system integration for high capacity load sensing. The system includes; a micromachined sensing gauge, interface electronics and a packaging module representing a system-in-package ready for end characterization. The various subsystems are presented in a modular stacked form using hybrid technologies. The micromachined sensing subsystem works on principles of piezo-resistive sensing and is fabricated using CMOS compatible processes. The structural configuration of the sensing layer is designed to reduce the offset, temperature drift, and residual stress effects of the piezo-resistive sensor. ANSYS simulations are carried out to study the effect of substrate coupling on sensor structure and its sensitivity. The load cell system has built-in electronics for signal conditioning, processing, and communication, taking into consideration the issues associated with resolution of minimum detectable signal. The packaged system represents a compact and low cost solution for high capacity load sensing in the category of compressive type load sensor.

Recent Advances in Low CTE and High Density System-on-a-Package (SOP) Substrate with Thin Film Component Integration

The Packaging Research Center has been developing next generation system-on-a-package (SOP) technology with digital, RF, optical, and sensor functions integrated in a single package/module. The goal of this effort is to develop a platform substrate technology providing very high wiring density and embedded thin film passive and active components using PWB compatible materials and processes. The latest SOP baseline process test vehicle has been fabricated on novel Si-matched CTE, high modulus C-SiC composite core substrates using 10mum thick BCB dielectric films with loss tangent of 0.0008 and dielectric constant of 2.65. A semi-additive plating process has been developed for multilayer microvia build-up using BCB without the use of any vacuum deposition or polishing/CMP processes. PWB and package substrate compatible processes such as plasma surface treatment/desmear and electroless/electrolytic pulse reverse plating was used. The smallest line width and space demonstrated in this paper is 6mum with microvia diameters in the 15-30mum range. This build-up process has also been developed on medium CTE organic laminates including MCL-E-679F from Hitachi Chemical and PTFE laminates with Cu-Invar-Cu core. Embedded decoupling capacitors with capacitance density of >500nF/cm2 have been integrated into the build-up layers using sol-gel synthesized BaTiO3 thin films (200-300nm film thickness) deposited on copper foils and integrated using vacuum lamination and subtractive etch processes. Thin metal alloy resistor films have been integrated into the SOP substrate using two methods: (a) NiCrAlSi thin films (25ohms per square) deposited on copper foils (Gould Electronics) laminated on the build-up layers and two step etch process for resistor definition, and (b) electroless plated Ni-W-P thin films (70 ohms to few Kohms per square) on the BCB dielectric by plasma surface treatment and activation. The electrical design and build-up layer structure along- - with key materials and processes used in the fabrication of the SOP4 test vehicle were presented in this paper. Initial results from the high density wiring and embedded thin film components were also presented. The focus of this paper is on integration of materials, processes and structures in a single package substrate for system-on-a-package (SOP) implementation

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.