Thu, 4 Apr 2014
Root Cause Deconvolution (RCD), a statistical enhancement technology recently made available in Mentor Graphics’ Tessent Diagnosis and YieldInsight products, is the next step in diagnosis resolution enhancement. It works by analyzing multiple layout-aware diagnosis reports together to identify the underlying defect distribution (root cause distribution) that is most likely to explain this set of diagnosis results. The results are then back- annotated to the individual diagnosis suspects.
Tue, 10 Oct 2014
The development of epoxy based underfill encapsulants marked a turning point for flip chip technology, and the semiconductor industry. Underfill encapsulants are carefully formulated to ensure flowability, an acceptable CTE, and other desirable properties. In this white paper, we explore what properties are required for effective underfills to ensure reliability and quality in flip chip applications.
Fri, 1 Jan 2015
Gain a better understanding about glass transition temperature (Tg) and why it is one of many factors to consider for bonding, sealing, coating and encapsulation applications. In this paper, we explore how temperature impacts the performance of polymers, why glass transition temperature is significant, and how it is measured. Tg can be an extremely useful yardstick for determining the reliability of epoxies as it pertains to temperature.
Fri, 3 Mar 2015
The research group led by Professor Peter Kinget at the Columbia University Integrated Systems Laboratory (CISL) focuses on cutting edge analog and RF circuit design using digital nanoscale CMOS processes. Key challenges in the design of these circuits include block-level characterization and full-circuit verification. This paper highlights these verification challenges by discussing the results of a 2.2 GHz PLL LC-VCO, a 12-bit pipeline ADC, and an ultra-wideband transceiver.
Fri, 2 Feb 2015
The use of imaging colorimeter systems and analytical software to assess display brightness and color uniformity, contrast, and to identify defects in FPDs is well established. A fundamental difference between imaging colorimetry and traditional machine vision is imaging colorimetry's accuracy in matching human visual perception for light and color uniformity. This white paper describes how imaging colorimetry can be used in a fully-automated testing system to identify and quantify defects in high-speed, high-volume production environments.