By Topic

Maximizing productivity improvements using short cycle time manufacturing (SCM) concepts in a semiconductor manufacturing line

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Martin, D.P. ; IBM Microelectron. Div., Essex Junction, VT, USA

Short cycle time manufacturing (SCM) has been developed to help understand and measure the components of line performance and of capacity loss. Central to SCM are the concepts that each line/toolset can be described by a curve relating normalized cycle time (X-factor) to throughput and that the position of this curve is determined by the capacity loss components. Productivity improvements are accomplished by reducing capacity loss components, resulting in a shift of this curve. This new curve can be used either to achieve more output or to lower cycle times. Line performance components are measured through the use of X-factor Contribution (XFC), which determines the contribution of each toolset to the overall line performance. Capacity loss components are measured through Cac-tus, which is an analysis tool that measures all the capacity loss components for each toolset in the line. This paper describes a further development of SCM that combines the results of the line performance components from X-factor contribution and capacity components from Cac-tus, and then compares them to their planned values. Each toolset in the manufacturing line is rank ordered by its X-factor contribution delta to plan. In this way, those toolsets whose actual X-factor performance is significantly different from plan are identified. Then, each toolset has a Cac-tus determined set of capacity components (e.g. tool availability, operator availability, etc.) that are also compared to plan, component by component. The importance of this step is that different organizations are responsible for fixing different kinds of problems. For example, engineering clearly owns fixing tool availability problems whereas manufacturing clearly owns fixing operational losses associated with staffing levels or work methods. The result of this analysis is an ordered list of toolsets contributing to line performance, clearly identified capacity components that are driving this performance loss, and the appropriate owners between engineering and manufacturing assigned to fix these problems. The result is a very efficient and focused system that maximizes productivity improvements by ensuring the right people work on the right problems

Published in:

Advanced Semiconductor Manufacturing Conference and Workshop, 2000 IEEE/SEMI

Date of Conference:

2000