Semicon Southeast Asia
April 25 - 27, 2017
Rudolph is excited to join Semicon Southeast Asia in 2017! Find us in the concourse, booth C149 to talk with one of our experts regarding your application needs.
Join the Discussion!
Semiconductor Yield Management Solutions Forum
Thursday, April 27 | 10:00am
Yield analysis and effective inline defect identification and isolation are critical processes in ensuring high yield and reliability in finished semiconductor products. Complex variability issues that involve interactions between manufacturing process and layout features can mask systematic yield issues. Without improved yield analysis methods, time-to-volume is delayed, mature yield is suboptimal, and product quality may suffer, thereby threatening a manufacturer’s profitability. Yield methodology that leverages inline production test results, volume scan diagnosis, and statistical analysis to identify the cause of yield loss is therefore pertinent to ensuring early extraction of actionable plan to address any excursions. This forum is targeted to delve into the technical forefront of inline yield diagnostics.
The Importance of Digital Threading: Connecting the Dots to Continuously Drive Inline Defectivity Advancements
Sonu Maheshwary, Rudolph Technologies
IC wafer fabrication complexity is driving the need for new and increasingly advanced analytical solutions to ensure products are delivered on time and with the highest possible quality. In the future, it will become essential for companies to produce digital threads linking all relevant information into product signatures. These product signatures can then be diagnosed and translated into actionable intelligence throughout the fabrication process. However, to realize this end-state, companies need overcome the challenge of linking wafer level defectivity to equipment level signals.
Creating a comprehensive genealogy is necessary to establish an effective digital fingerprint for yield degrading inline defectivity. This requires the ability to synchronize positional defect signatures with time series based equipment signal processing. By leveraging machine learning and associated technologies, companies can quickly transform their inline defectivity and equipment level data into actionable intelligence, accelerating their transformation to the predictive and prescriptive analytics-based manufacturing paradigm.
With the goal of establishing real-time situational awareness for improved inline defectivity decision-making, enlightened companies will embrace this next stage of industry evolution – the migration to a connected digital environment that enables a flexible, adaptable and intelligent wafer fabrication system. The resulting ROI from this type of investment will continue the industry’s long term trend of delivering the right products, at the right time and with the requisite economics to ensure both consumer and commercial adoption.