Risk mitigation framework for a robust design process

The increasing complexity of new manufacturing processes and the continuously growing range of fabrication options mean that critical decisions about the insertion of new technologies must be made as early as possible in the design process. Mitigating the technology risks under limited knowledge is a key factor and major requirement to secure a successful development of the new technologies. In order to address this challenge, a risk mitigation methodology that incorporates both qualitative and quantitative analysis is required. This paper outlines the methodology being developed under a major UK grand challenge project - 3D-Mintegration. The main focus is on identifying the risks through identification of the product key characteristics using a product breakdown approach. The assessment of the identified risks uses quantification and prioritisation techniques to evaluate and rank the risks. Traditional statistical process control based on process capability and six sigma concepts are applied to measure the process capability as a result of the risks that have been identified. This paper also details a numerical approach that can be used to undertake risk analysis. This methodology is based on computational framework where modelling and statistical techniques are integrated. Also, an example of modeling and simulation technique is given using focused ion beam which is among the investigated in the project manufacturing processes.

Optimisation methodology for risk mitigation in advanced micro-system electronics manufacturing

This paper presents a design methodology based on numerical modelling, integrated with optimisation techniques and statistical methods, to aid the development of new advanced technologies in the area of micro and nano systems. The design methodology is demonstrated for a micro-machining process called Focused Ion Beam (FIB). This process has been modelled to provide knowledge of how a pre-defined geometry can be achieved through this direct milling. The geometry characterisation is obtained using a Reduced Order Models (ROM), generated from the results of a mathematical model of the Focused Ion Beam, and Design of Experiment (DoE) methods. In this work, the focus is on the design flow methodology which includes an approach on how to include process parameter uncertainties into the process optimisation modelling framework. A discussion on the impact of the process parameters, and their variations, on the quality and performance of the fabricated structure is also presented. The design task is to identify the optimal process conditions, by altering the process parameters, so that certain reliability and confidence of the application is achieved and the imposed constraints are satisfied. The software tools used and developed to demonstrate the design methodology are also presented.