CITCOM: A complimentary inspection technique based on computer tomography and plenoptic camera for MEMS components
For the latest generation of micro-fabricated devices that are currently being developed, no suitable in-line production inspection equipment is available, simply because current inspection equipment expects planar processing while most of the devices are often highly 3D in nature e.g. medical. This lack of automated processing feedback makes it difficult to steer process development towards higher yields in micro-components and MEMS production. Another visible problem is the need to document and record process data, even on the individual device level, with the degree of traceability as is required for example, for medical devices fabricated under ISO13485. Both factors in the end limit the possibility of reliable and cost effective manufacturing of MEMS and micro-components.
Thus, CITCOM has been proposed to address the industrial needs of MEMS and micro-manufacturing which will offer an in-line production inspection and measurement system for micro-components. The system will be developed and demonstrated at TRL7. The system will be based on optical and X-ray techniques combined with computer tomography and advance robotic system capable of analyzing defects that occur in production of micro components e.g. stains, debris, fracture, abnormal displacements, chemical composition of surface coatings, surface traces etc. enabling 98% yield and 100% reliability.
Ultimately, CITCOM will cut such costs by 60% as it will offer a system with automated knowledge and inspection data based process feedback that will allow the detection and traceability of faults that may occur in MEMS production, especially for critical applications like aerospace, space and healthcare.
CITCOM will give Europe a technological and competitive advantage in the growing manufacturing and production industry. The consortium behind this action is strongly driven by industrial need and problem having Philips and Microsemi as end users and validators of the technology.
The objective is to develop an in-line inspection, measurement and control system for high reliability and detailed monitoring and inspection of MEMS, micro-components and micro-devices based on a 3D optical quality inspection system (in-line) and nano-focused 2D X-ray system (near-line).
By using CITCOM system manufacturing process of micro-components and MEMs will become efficient, and enhanced as CITCOM will offer:
- Improved productivity and Yield: The use of CITCOM inspection technology can transform quality control management by simplifying and streamlining complex production processes. It can deliver greater workflow capabilities and provide increased support for data-driven decision-making when and where it is needed, improving overall collaboration. CITCOM system will offer 98% output yield and for some micro-components 100% yield thus enhancing productivity and resource efficiency of overall validation and production by 90%.
- Increased responsiveness: CITCOM inspection applications provide greater efficiency in transferring data from the shop floor to stakeholders, leading to faster resolution of defects and problems on and off the production line.
- Improved work quality: On- and off-site auditing supported by CITCOM inspection technology provides better integrity of information and standardization across multiple locations—ensuring that both data collected and processes followed are consistent throughout the enterprise. This capability reduces the chance of human error, resulting in higher quality performance and lower cost-of-quality delivery.
- Advanced competitive advantage: Operational advantages derived from CITOM inspections help accelerate time to market, control costs and provide capabilities that improve the customer experience—resulting in key competitive benefits for your business.
Brunel Innovation Centre's Role
- Defect detection and localisation using image processing and machine learning for optical images
- Defect identification using machine learning for optical images
- Defect detection and localisation using image processing and machine learning for x-ray images
- Defect identification using machine learning for x-ray images
- Image stitching (image processing)
- Brunel University London
Meet the Principal Investigator(s) for the project
Professor Tat-Hean Gan
- Professional Qualifications - CEng. IntPE (UK), Eur Ing, BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham), MSc in Advanced Mechanical Engineering (University of Warwick), MBA in International Business (University of Birmingham), PhD in Engineering (University of Warwick), Languages - English, Malaysian, Mandarin, Cantonese, Professional Bodies - Fellow of the British Institute of NDT, Fellow of the Institute of Engineering and Technology, Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.