Corie Lynn Cobb

Case-Based Reasoning for Evolutionary MEMS Design

A knowledge-based computer-aided design tool for microelectromechanical systems (MEMS) design synthesis called case-based synthesis of MEMS (CaSyn-MEMS) has been developed. MEMS-based technologies have the potential to revolutionize many consumer products and to create new market opportunities in areas such as biotechnology, aerospace, and data communications. However, the commercialization of MEMS still faces many challenges due to a lack of efficient computer-aided design tools that can assist designers during the early conceptual phases of the design process. CaSyn-MEMS combines a case-based reasoning (CBR) algorithm and a MEMS case library with parametric optimization and a multi-objective genetic algorithm (MOGA) to synthesize new MEMS design topologies that meet or improve upon a designers specifications. CBR is an artificial intelligence methodology that uses past design solutions and adapts them to solve current problems. Having the ability to draw upon past design knowledge is advantageous to MEMS designers, allowing reuse and modification of previously successful designs to accelerate the design process. To enable knowledge reuse, a hierarchical MEMS case library has been created. A reasoning algorithm retrieves cases with solved problems similar to the current design problem. Focusing on resonators as a case study, case retrieval demonstrated an 82% success rate. Using the retrieved cases, approximate design solutions were proposed by first adapting cases with parametric optimization, resulting in a 25% reduction in design area on average while bringing designs within 2% of the frequency goal. In situations where parametric optimization was not sufficient, a more radical design adaptation was performed through the use of MOGA. CBR provided MOGA with good starting points for optimization, allowing efficient convergence to higher quantities of Pareto optimal design concepts while reducing design area by up to 43% and meeting frequency goals within 5%.

Case-Based Reasoning for the Design of Micro-Electro-Mechanical Systems

Although Micro-Electro-Mechanical Systems (MEMS) are forming the basis for a rapidly growing industry and fields of research, many MEMS designers still rely on back-of-the-envelope calculations due to a lack of efficient computer-aided design (CAD) tools that can assist with the initial stages of design exploration. This paper introduces case-based reasoning (CBR) techniques to the design of MEMS, as part of a larger MEMS synthesis framework currently under development at UC Berkeley. Having the ability to draw upon past design knowledge is advantageous to the MEMS designer, allowing reuse and modification of previous successful designs to help deal with the complexities of a new design problem. CBR utilizes past successful MEMS designs and sub-assemblies as building blocks stored in an indexed library. Reasoning tools find cases in the library with solved problems similar to the current design problem in order to propose promising conceptual designs. This paper discusses case representation and case library design as well as the results of case retrieval studies, focusing on MEMS resonant structures. The paper recommends strategies for integrating the MEMS case library with evolutionary computation when parameter optimization over the retrieved conceptual designs is not sufficient or there are gaps of knowledge in the case library.Copyright

MEMS Design Synthesis: Integrating Case-based Reasoning and Multi-objective Genetic Algorithms

A case-based reasoning (CBR) knowledge base has been incorporated into a Micro-Electro-Mechanical Systems (MEMS) design tool that uses a multi-objective genetic algorithm (MOGA) to synthesize and optimize conceptual designs. CBR utilizes previously successful MEMS designs and sub-assemblies as building blocks stored in an indexed case library, which serves as the knowledge base for the synthesis process. Designs in the case library are represented in a parameterized object-oriented format, incorporating MEMS domain knowledge into the design synthesis loop as well as restrictions for the genetic operations of mutation and crossover for MOGA optimization. Reasoning tools locate cases in the design library with solved problems similar to the current design problem and suggest promising conceptual designs which have the potential to be starting design populations for a MOGA evolutionary optimization process, to further generate more MEMS designs concepts. Surface micro-machined resonators are used as an example to introduce this integrated MEMS design synthesis process. The results of testing on resonator case studies demonstrate how the combination of CBR and MOGA synthesis tools can help increase the number of optimal design concepts presented to MEMS designers.

Longitudinal Study of Learning Outcomes in a New Product Development Class

This paper reports on a longitudinal study of lessons learned from a graduate-level New Product Development course taught at the University of California at Berkeley, comparing lessons learned by students during the course with alumni perceptions one to ten years after graduation. Previous research on student learning outcomes in New Product Development (NPD) found that on the last day of class students identify working in multifunctional teams and understanding user needs as their most important lessons learned. This study raises the question of whether or not students maintain the same emphasis on learning outcomes once they have moved on to careers in industry. To answer this question, we conducted 21 in-depth interviews with alumni who took the course between 1995–2005 and are now working in industry. A qualitative and quantitative analysis of the alumni interviews reveals that former students still highly value what they learned about team work and understanding user needs, but see more value in tools for concept generation, prototyping, and testing after gaining work experience. The results reaffirm the value of engaging students in multidisciplinary design projects as a vehicle for developing the professional skills needed in today’s competitive new product development environment.Copyright

Knowledge-Based Evolutionary Linkage in MEMS Design Synthesis

Multi-objective Genetic Algorithms (MOGA) and Case-based Reasoning (CBR) have proven successful in the design of MEMS (Micro-electro-mechanical Systems) suspension systems. This work focuses on CBR, a knowledge-based algorithm, and MOGA to examine how biological analogs that exist between our evolutionary system and nature can be leveraged to produce new promising MEMS designs. Object-oriented data structures of primitive and complex genetic algorithm (GA) elements, using a component-based genotype representation, have been developed to restrict genetic operations to produce feasible design combinations as required by physical limitations or practical constraints. Through the utilization of this data structure, virtual linkage between genes and chromosomes are coded into the properties of pre-defined GA objects. The design challenge involves selecting the right primitive elements, associated data structures, and linkage information that promise to produce the best gene pool for new functional requirements. Our MEMS synthesis framework, with the integration of MOGA and CBR algorithms, deals with the linkage problem by integrating a component-based genotype representation with a CBR automated knowledge-base inspired by biomimetic ontology. Biomimetics is proposed as a means to examine and classify functional requirements so that case-based reasoning algorithms can be used to map design requirements to promising initial conceptual designs and appropriate GA primitives. CBR provides MOGA with good linkage information through past MEMS design cases while MOGA inherits that linkage information through our component-bsased genotype representation. A MEMS resonator test case is used to demonstrate this methodology.

Developments in MEMS scale printable alkaline and Li-ion technology

Two technologies for MEMS (Microelectromechanical Systems) scale cell formation are discussed. First, the fabrication of planar alkaline cell batteries compatible with MEMS scale power storage applications is shown. Both mm scale and sub-mm scale individual cells and batteries have been constructed. The chosen coplanar electrode geometry allows for easy fabrication of series connected cells enabling higher voltage while simplifying the cell sealing and electrode formation. The Zn/Ag alkaline system is used due to the large operating voltage, inherent charge capacity, long shelf life, and ease of fabrication. Several cells have been constructed using both plated and spun-on silver. The plated cells are shown to be limited in performance due to inadequate surface area and porosity; however, the cells made from spun-on colloidal silver show reasonable charge capacity and power performance with current densities of up to 200 uA/mm2 and charge capacities of up to 18 mA-s/mm2. Second, a new printing method for interdigitated 3-D cells is introduced. A microfluidic printhead capable of dispensing multiple materials at high resolution and aspect ratio is described and used to form fine interdigitated cell features which show >10 times improvement in energy density. Representative structures enabled by this method are modeled, and the energy and power density improvements are reported.

Case-based reasoning and object-oriented data structures exploit biological analogs to generate virtual evolutionary linkages

Multiobjective genetic algorithms (MOGA) and case-based reasoning (CBR) have proven successful in the design of MEMS (microelectromechanical systems) suspension systems. Object-oriented data structures of primitive and complex genetic algorithm (GA) elements have been developed to restrict genetic operations to produce feasible design combinations as required by physical limitations or practical constraints. Thus, virtual linkage between genes and chromosomes are coded into the properties of pre-defined GA objects. A new design problem requires selecting the right primitive elements, associated data structures, and linkages that promise to produce the best gene pool for new functional requirements. In this paper, biomimetics is proposed as a means to examine and classify functional requirements so that case-based reasoning algorithms can be used to map design requirements to promising initial conceptual designs and appropriate GA primitives. The concept is demonstrated using micro-mechanical resonators.