5 Reasons Why Defect Reduction Is Critical In Semiconductor Material Success


Semiconductors may be small, but the impacts they have are significant. Semiconductors used in life-dependent applications, such as pacemakers, defibrillators, life support systems, automotive safety systems, or in aviation need to be fail-proof. A device smaller than a centimeter with features just a few nanometers has no margin of error. This blog shares why it’s important to detect materia... » read more

SiC Growth For EVs Is Stressing Manufacturing


The electrification of vehicles is fueling demand for silicon carbide power ICs, but it also is creating challenges in finding and identifying defects in those chips. Coinciding with this is a growing awareness about just how immature SiC technology is and how much work still needs to be done — and how quickly that has to happen. Automakers are pushing heavily into electric vehicles, and t... » read more

Striking A Balance In Acoustic Inspection


Sound energy is a quick way to to spot voids, delamination, cracks, and other possible defects that are accessible from outside the chip or package, as well as some defects that are inside of chips. But acoustic inspection also is highly sensitive to different materials with different polarities, which can change the reflection of sound waves. Bill Zuckerman, product marketing manager at Nordso... » read more

Journey From Cell-Aware To Device-Aware Testing Begins


Early results of using device-aware testing on alternative memories show expanded test coverage, but this is just the start. Once the semiconductor industry realized that it was suffering from device failures even when test programs achieved 100% fault coverage, it went about addressing this disconnect between the way defects manifest themselves inside devices and the commonly used fault mod... » read more

Ramping Up Power Electronics For EVs


The rapid acceleration of the power devices used in electric vehicles (EVs) is challenging chipmakers to adequately screen the ICs that power these vehicles.[1] While progress toward autonomous driving is grabbing the public’s attention, the electrification of transportation systems is progressing quietly. For the automotive industry, this shift involves a mix of electronic components. Amo... » read more

Data Analytics For The Chiplet Era


This article is based on a paper presented at SEMICON Japan 2022. Moore’s Law has provided the semiconductor industry’s marching orders for device advancement over the past five decades. Chipmakers were successful in continually finding ways to shrink the transistor, which enabled fitting more circuits into a smaller space while keeping costs down. Today, however, Moore’s Law is slowin... » read more

EUV Lithography: Results of Single Particle Volume Charging Processes in EUV Exposure Environment With Focus On Afterglow Effects


A new technical paper titled "Particle charging during pulsed EUV exposures with afterglow effect" was published by researchers at ASML, ISTEQ B.V., and Eindhoven University of Technology. Abstract "The nanoparticle charging processes along with background spatial-temporal plasma profile have been investigated with 3DPIC simulation in a pulsed EUV exposure environment. It is found that the ... » read more

What Data Center Chipmakers Can Learn From Automotive


Automotive OEMs are demanding their semiconductor suppliers achieve a nearly unmeasurable target of 10 defective parts per billion (DPPB). Whether this is realistic remains to be seen, but systems companies are looking to emulate that level of quality for their data center SoCs. Building to that quality level is more expensive up front, although ultimately it can save costs versus having to ... » read more

Screening For Silent Data Errors


Engineers are beginning to understand the causes of silent data errors (SDEs) and the data center failures they cause, both of which can be reduced by increasing test coverage and boosting inspection on critical layers. Silent data errors are so named because if engineers don’t look for them, then they don’t know they exist. Unlike other kinds of faulty behaviors, these errors also can c... » read more

Active Learning to Reduce Data Requirements For Defect Identification in Semiconductor Manufacturing


A new technical paper titled "Exploring Active Learning for Semiconductor Defect Segmentation" was published by researchers at Agency for Science, Technology and Research (A*STAR) in Singapore. "We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance. To address these challenges, we propose to perform contrastive pretrainin... » read more

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