« FIABILISATION & INDUSTRIALIZATION OF RISK ANALYSIS METHODS » A. MILI 1 ; S. HUBAC 1 ; S. BASSETTO 2 ; ; 1 STMicroelectronics – 850 rue Jean Monnet, Crolles Cedex. 2 Laboratoire des Sciences pour la Conception, l'Optimisation et la Production - ENSGI-INPG, 46 Avenue Félix Viallet, Grenoble. We are at early phase of deployment. The futur model for risk management is complete. It consist of capitalizing in a data base the information concerning breakdowns and the risk analysis data in the objective of facilitating their exploitation. We have chosen a pilot workshop to start deployement and test the level of data transfert from CMMS data base to risk management system. We search after the test period to duplicate this … I- Introduction Context: - Very important equipment cost - Wafer production costs - Variability of scrap number per month  Continuous control and tool improvement to achieve the objectives in term of quality and due dates.  Using powerful means of control and monitoring in real times and standard methods of risk analysis management (FMEA – Failure Mode and Effects Analysis, 8D…) Objectives: - Minimizing tool variability and remove accidental breakdowns - Deployement of the risk analysis method (FMEA) within Crolles2 alliance - Initiating a system for automatic risk analyses update according to events related to the process control in line. II- FMEA Management The aim of this study consist on setting up a risk management system within Crolles2 Alliance. Based on a standard method (FMEA), it was applied on three levels: products (FMEA Recipe ), processes (FMEA Module ) and equipment (FMEA Block Hardware ). model for all workshops and the goal is to have a standart method for all. In second step, we aim to update our risk data base with informations of defects on product, client reclaim and all data at the origin of real or imminent risk. This new method allow us to quantify the benefit of risk analysis method on process. It symplifies also the control of the actions through reports generated automatically from database. V- Conclusion Use of FDC parameters: We propose to establish the link between FDC variables and the risk detection in the FMEA to set up a function g which allows starting from the whole variables of the machine (V = {V1, V2,… Vn}), the fulfilled function (F) and the risk (R), to determine the subset of potential parameters (X) at the origin of the dysfunction. III- APPROACH - Define the priority of actions axes - Use equipment events to define real or emminent risk - Standardise the language of the three parts: FMEA, maintenance interventions and FDC- Fault Detection and Control. A prototype application was developed allowing the automatic re-evaluation of the risks analyses according to the machines breakdowns. The coding used in the CMMS and the FMEA being very similar, the automatic translation could be carried out. The test of this operation during 100 days, shows that 6670 risks were generated, 4923 were identified beforehand. Their occurrences were updated, and 1747 new risks were identified, among which some with evaluation exceeding the threshold of action's release (RPN > 125). Fig 2. Risk management model Fig 1. Scrap evolution UNSHEDULED DOWN Maintenance LITHO REPORT ( PARETO OF FAILURE) REPORT ( PARETO OF FAILURE) ENG. REPORT CMMS FMEA Report (FMEA BH) Incoherent data with FMEA Formalism Inalienable information in risk analysis Fig 3. Maintenance data and FMEA Block Hardware Fig 4. Link between CMMS date and FMEA analyses Process Tool (F) X = g (F, V, R) Silver Box V = {V 1, V 2, …, V n } Fig 5. Control parameters