End-point detection using focused ion beam-excited photoemissions in milling deep small holes in large scale integrated circuit structures

1998 ◽  
Vol 16 (4) ◽  
pp. 2555 ◽  
Author(s):  
Hiroshi Yamaguchi
Keyword(s):  
Integrated Circuit ◽  
Large Scale ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
End Point ◽  
End Point Detection ◽  
Point Detection ◽  
Small Holes

Second Order Effect Monitoring During Backside Milling for Functional End Point Detection

2019 ◽  
Author(s):  
Anthony George ◽  
Isaac Goldthwaite ◽  
Katie Liszewski ◽  
Jeremiah Schley ◽  
Thomas Kent
Keyword(s):  
Integrated Circuit ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Second Order ◽  
Order Effects ◽  
Back Side ◽  
End Point ◽  
End Point Detection ◽  
Second Order Effects ◽  
Point Detection

Abstract Backside silicon removal provides an avenue for a number of modern non-destructive and circuit edit techniques. Visible light microscopy, electron beam microscopy, and focused ion beam circuit edit benefit from a removal of back side silicon from the integrated circuit being examined. Backside milling provides a potential path for rapid sample preparation when thinned or ultrathinned samples are required. However, backside milling is an inherently destructive process and can damage the device function, rendering it no longer useful for further nondestructive analysis. Recent methods of backside milling do not guarantee device functionality at a detected end point without a priori knowledge. This work presents a methodology for functional end point detection during backside milling of integrated circuit packaging. This is achieved by monitoring second order effects in response to applied device strain, which guide the milling procedure, avoiding destructive force as the backside material is removed. Experimental data suggest a correlation between device power consumption waveforms and second order effects which inform an in situ functional end point. Keywords: functional end point, side-channel analysis, backside thinning, milling, machine learning, second order effects

Automated End-Point Detection and Targeted Ar+ Milling of Advanced Integrated Circuit FIB TEM Specimens

2017 ◽  
Author(s):  
C.S. Bonifacio ◽  
P. Nowakowski ◽  
M.J. Campin ◽  
J.T. Harbaugh ◽  
M. Boccabella ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Specimen Preparation ◽  
Ion Milling ◽  
Transmission Electron ◽  
End Point Detection ◽  
Point Detection ◽  
Argon Ion

Abstract The sub-nanometer resolution that transmission electron microscopy (TEM) provides is critical to the development and fabrication of advanced integrated circuits. TEM specimens are usually prepared using the focused ion beam, which can cause gallium-induced artifacts and amorphization. This work presents the use of a concentrated argon ion beam for reproducible TEM specimen preparation using automatic milling termination and targeted ion milling of device features; the result is high-quality and electron-transparent specimens of less than 30 nm. Such work is relevant for semiconductor product development and failure analysis.

Focused Ion Beam Sims for Micromachining Applications

1987 ◽  
Vol 101 ◽  
Author(s):  
L. R. Harriott ◽  
M. J. Vasile
Keyword(s):  
Focused Ion Beam ◽  
Secondary Ion Mass Spectrometry ◽  
Ion Beam ◽  
High Yield ◽  
Ion Milling ◽  
End Point ◽  
Glass Interface ◽  
End Point Detection ◽  
Point Detection ◽  
Secondary Ion

ABSTRACTA secondary ion mass spectrometry (SIMS) system has been incorporated into the AT&T-BL second generation focused ion beam (FIB) micromachining system. The primary applications are end-point detection and topographical element mapping. End-point detection of Cr micromachining on photomasks was done with raster sizes ranging from 10 μm x 10 μm to 3 μm x 3 μm. SIMS end-points, total ions images, and transmitted light measurements show that the ion-milling can be controlled to stop prior to or after the Cr/glass interface. Mass selected secondary ion images have been obtained for high yield ions such as52Cr+ and27 A1+ on raster fields of 25 μm in time intervals ranging from 20 to 100 sec. Al+ SIMS images of 1 μm lines and spaces from a VLSI test pattern have been obtained.

Electrical end-point detection during ion beam etching of thin films and multilayers

1995 ◽  
Vol 8 (8) ◽  
pp. 676-679 ◽  
Author(s):  
A Matthes ◽  
F Schmidl ◽  
K -U Barholz ◽  
F Elschner ◽  
H Schneidewind ◽  
...  
Keyword(s):  
Thin Films ◽  
Ion Beam ◽  
Ion Beam Etching ◽  
End Point ◽  
End Point Detection ◽  
Point Detection

Advanced sub 0.13µm Cu Devices – Failure Analysis and Circuit Edit With Improved FIB Chemical Processes and Beam Characteristics

2002 ◽  
Author(s):  
J. David Casey ◽  
Thomas J. Gannon ◽  
Alex Krechmer ◽  
David Monforte ◽  
Nicholas Antoniou ◽  
...  
Keyword(s):  
Failure Analysis ◽  
High Speed ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Chemical Processes ◽  
Design Verification ◽  
End Point Detection ◽  
On Chip ◽  
Point Detection ◽  
Low K

Abstract Advances in FIB (focused ion beam) chemical processes and in the Ga (gallium) beam profile are discussed; these advances are necessary for the successful failure analysis, circuit edit and design verification of advanced, sub-0.13µm Cu devices. Included in this article are: a novel FIB method (CopperRx) for smoothly milling thick, large grained Cu lines; H2O and O2 processes for cleanly cutting thin, smaller grained Cu lines, thereby forming electrically open interconnects; a XeF2 GAE (gas assisted etching) process for etching low k, CVD dielectrics such as F and C doped SiO2; H2O and XeF2 GAE processes for etching low k, spin-on, organic dielectrics such as SiLK; a recently developed recipe for the deposition of SiO2 based material with intermediate resistivity (106 µohm·cm) which is useful in the design verification of frequency sensitive, high speed analog and SOC (system on chip) circuits; an improved, more Gaussian Ga beam with less current density in the beam tails (VisION column) which provides higher resolution, real time images needed for end-point detection on sub 0.13µm features during milling.

Analysis of Successive Focused Ion Beam Slices by Scanning Electron Imaging and 3D Reconstruction

2005 ◽  
Vol 908 ◽  
Author(s):  
Terence Yeoh ◽  
Neil Ives ◽  
Nathan Presser ◽  
Gary Stupian ◽  
Martin Leung ◽  
...  
Keyword(s):  
Focused Ion Beam ◽  
Ion Beam ◽  
Three Dimensional ◽  
Complex Mixture ◽  
Dimensional Reconstruction ◽  
Microscope Imaging ◽  
End Point Detection ◽  
Electron Imaging ◽  
Point Detection ◽  
Scanning Electron

AbstractAn antifuse structure was analyzed using scanning electron microscope imaging and focused ion beam image slicing to generate a form of three-dimensional microscopy. This method reveals nanometer scale features that could not be easily imaged using a single focused ion beam cross-section. A novel end-point detection technique has been developed to control the thickness of the slice to about 2 nm. Voxel imaging and interpretive three-dimensional reconstruction was used to resolve volumes as small as 2 cubic nm3. It was determined that the fusing region for an antifuse is a complex mixture of material phases with an elliptical volume approximately 75 nm in diameter.

scholarly journals Dielectric Film Thickness Measurement Via a Convolutional Neural Network for Integrated Circuit Delayering End Point Detection

2021 ◽  
Author(s):  
Jonathan Scholl ◽  
Nick Darby ◽  
Josh Baur ◽  
Yash Patel ◽  
Isabel Boona ◽  
...  
Keyword(s):  
Computer Vision ◽  
Film Thickness ◽  
Integrated Circuit ◽  
Dielectric Film ◽  
Ideal Point ◽  
Thickness Measurement ◽  
Dielectric Thickness ◽  
End Point ◽  
End Point Detection ◽  
Point Detection

Abstract The integrated circuit (IC) delayering workflow is heavily reliant on operator experience to determine the processing end point, which is the ideal point on an IC where processing should be terminated, to optimize region of interest imaging. The current method of end point detection during IC delayering utilizes qualitative correlation between dielectric film color and dielectric thickness observed via optical microscopy to guide decision making. The goal of this work is to quantify this relationship using computer vision. In the field of computer vision, convolutional neural networks (CNNs) have been successfully applied to capture spatial relationships within images. Given this success, a CNN was trained for thickness estimates of dielectric films using optical images captured during processing for eventual automated end point detection. The trained model explained 39% of the variance in dielectric film thickness with a mean absolute error of approximately 47 nm.

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