Semiconductor & Computer Engineering
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== Overview == | == Overview == | ||
Ramping a new [[technology node]] in a [[fabrication plant]] refers to the process of scaling up production from low-volume, prototype manufacturing, in laboratory-like settings to [[high-volume manufacturing]] (HVM) in order to reach economies of scale. For a [[leading-edge node]], ramping is a complex process that also involves overcoming the countless discrepancies that arise between the way the process is designed to work as defined in the [[process specifications]] and how the process is actually operating in high volumes. During this time [[yield learning]] is applied in order to reduce or eliminate those inconsistencies, achieving higher production output. | Ramping a new [[technology node]] in a [[fabrication plant]] refers to the process of scaling up production from low-volume, prototype manufacturing, in laboratory-like settings to [[high-volume manufacturing]] (HVM) in order to reach economies of scale. For a [[leading-edge node]], ramping is a complex process that also involves overcoming the countless discrepancies that arise between the way the process is designed to work as defined in the [[process specifications]] and how the process is actually operating in high volumes. During this time [[yield learning]] is applied in order to reduce or eliminate those inconsistencies, achieving higher production output. | ||
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| + | == See also == | ||
| + | * {{intel|Copy Exactly!}} | ||
{{stub}} | {{stub}} | ||
Latest revision as of 11:26, 26 December 2018
Ramp-up or ramping is an early stage in the manufacturing process when production is being scaled to high-volume from a small, laboratory-like setting.
Overview[edit]
Ramping a new technology node in a fabrication plant refers to the process of scaling up production from low-volume, prototype manufacturing, in laboratory-like settings to high-volume manufacturing (HVM) in order to reach economies of scale. For a leading-edge node, ramping is a complex process that also involves overcoming the countless discrepancies that arise between the way the process is designed to work as defined in the process specifications and how the process is actually operating in high volumes. During this time yield learning is applied in order to reduce or eliminate those inconsistencies, achieving higher production output.
See also[edit]
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