A step toward capacity planning at finite capacity in semiconductor manufacturing

Abstract

International audienceProduction planning in the Semiconductor Industry (SI) has emerged as the most complex process due to its process complexity, technological constraints and high-mix low-volume production characteristics. In this paper, we present two different production planning approaches, developed by STMicroelectronics and G-SCOP research laboratory, to better control the production in 300mm production line at Crolles. At first, a mixed integer program (MIP) is proposed that projects the production lot trajectories (start and end dates) for the remaining subsequent steps, taking into account finite production capacities. A heuristic is then proposed to simplify the problem of finite capacity by neglecting equipment capacity. This approach results in the development of an infinite capacity WIP projection engine that complies with lots due dates and takes into account cycle time variability

A review of machine efficiency in mass customization

“…Author (Year) Efficiency-related issues in MC Benjamin et al (2013) Small stop time loss, startup rejects, production rejects, setup adjustment, breakdown, speed Choudhary and Shankar (2014) Lot size, delivery Mhiri et al (2014) Cycle time variability, more production steps; series, parallel, sequencedependent setups, equipment breakdown, re-entrant flow, production mix, production volume Mert et al (2014) Cutting speed Kinauer and Muller (2014) Breakdown Ng et al (2014) Equipment downtime Munirathinam and Ramadoss (2014) Over maintenance, under maintenance Caggiano (2014) Machine processing and failure rates, machine layout), the workload (e.g. arrivals of raw materials over time, part routings), and the operational rules (e.g.…”

“…The availability rate reduces due to work stoppage caused by the disruptions, as explained by Zhu (2011). Mhiri et al (2014) explained that breakdowns reduce production capacities. Denkena et al (2012) described that for a higher level of automation, the productiveness depends on availability.…”

A literature review on variability in semiconductor manufacturing: The next forward leap to Industry 4.0

“…As these types of projections are made considering finite horizons (e.g., see Mhiri et al 2014), the dynamic cycle time models to develop are closer to clearing functions (see Kacar et al 2012) than queuing theory. Using historical data to derive these models is not appropriate, since the behavior of the toolsets changes with the mix of products and lots priorities.…”

Rapid unconstrained fab model using a business intelligence tool: DM: Data management and data mining tools

“…While constrained models and simulation models are great tools the fact that they take hours to run & use renders them unusable if we need such a model to run many times in a day for multiple scenarios. Exact methods could not afford an optimal solution due to the huge number of variables, and constraints which increases problem complexity, and execution time (Mhiri et al, 2014). Spreadsheet approaches lack precision as they overestimate capacities and underestimate cycle times and are useful only for production lines having few different products and stable product mix (Ignizio and Garrido 2012).…”

“…Scope for future research includes WIP rebalancing based on equipment capacity / utilization. We are also considering refining the projections based on cycle time based Zachka formula (Mhiri et al, 2014) and queuing theory based Entitlement Cycle Time estimation technique (Leachman, 2012).…”