Automated nanoprobing under scanning electron microscopy

Gong, Zheng; Chen, Brandon K.; Liu, Jun; Sun, Yu

doi:10.1109/icra.2013.6630759

Cited by 4 publications

(3 citation statements)

References 18 publications

(17 reference statements)

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“…The table is moved in z direction to the probe until a significant scratch of the probe tips over the contact pads is detected. This method of detecting the physical contact between the tips and the pads was already validated by other groups [17]. A significant scratch in this case is defined with an absolute moving of the tips in X and Y direction.…”

Section: Vna Calibrationmentioning

confidence: 93%

Automated Calibration of RF On-Wafer Probing and Evaluation of Probe Misalignment Effects Using a Desktop Micro-Factory

Microrobotics

Oldenburg

2016

JCC

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A fully automatic setup for on-wafer contact probing will be presented. This setup consists of six automatable nano positioning axes used as tool holder and a sample holder. With this setup a fully automatic one-port SOL calibration for a Vector Network Analyzer is done. Furthermore a fully automated on-wafer contact probing is performed. Afterwards, the effects of a misalignment of the three tips of a GSG-probe are examined. Additionally the error on the calibration is calculated to determine its effect on the measurement. The results show, that a misalignment of the probe has a high impact on the measurement of the VNA. Hence a fully automated on-wafer probing presented in this paper is a good way to detect these misalignments and correct them if necessary.

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Section: Vna Calibrationmentioning

confidence: 93%

Automated Calibration of RF On-Wafer Probing and Evaluation of Probe Misalignment Effects Using a Desktop Micro-Factory

Microrobotics

Oldenburg

2016

JCC

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“…al, presented an automated nanohandling workstation for mechanical characterization of nanotubes by measuring Young’s modulus of multiwalled carbon nanotubes (MWCNTs) [ 9 ]. Gong et.al implemented visual tracking and servoing control of nanoprobes to perform robust and precise nanoprobing tasks on integrated circuits (IC) [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM

Ding

Shi

et al. 2018

Sensors

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The maneuvering and electrical characterization of nanotubes inside a scanning electron microscope (SEM) has historically been time-consuming and laborious for operators. Before the development of automated nanomanipulation-enabled techniques for the performance of pick-and-place and characterization of nanoobjects, these functions were still incomplete and largely operated manually. In this paper, a dual-probe nanomanipulation system vision-based feedback was demonstrated to automatically perform 3D nanomanipulation tasks, to investigate the electrical characterization of nanotubes. The XY-position of Atomic Force Microscope (AFM) cantilevers and individual carbon nanotubes (CNTs) were precisely recognized via a series of image processing operations. A coarse-to-fine positioning strategy in the Z-direction was applied through the combination of the sharpness-based depth estimation method and the contact-detection method. The use of nanorobotic magnification-regulated speed aided in improving working efficiency and reliability. Additionally, we proposed automated alignment of manipulator axes by visual tracking the movement trajectory of the end effector. The experimental results indicate the system’s capability for automated measurement electrical characterization of CNTs. Furthermore, the automated nanomanipulation system has the potential to be extended to other nanomanipulation tasks.

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“…The methods of FA have improved over the years, owing to the rapid development of advanced device analysis tools such as E-beam absorbed current, scanning transmission electron microscopy (STEM), − transmission electron microscopy (TEM), − nanoprobing, − laser voltage probing, photoemission electron microscopy, − and many more. , These tools provide critical information to help experts accurately identify the failure mode and mechanism, as shown in Figure b(iv,v).…”

mentioning

confidence: 99%

Transfer Learning-Based Artificial Intelligence-Integrated Physical Modeling to Enable Failure Analysis for 3 Nanometer and Smaller Silicon-Based CMOS Transistors

Pan

Low

Ghosh

et al. 2021

ACS Appl. Nano Mater.

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Integral to the success of the semiconductor industry in keeping up with Moore's law is the importance of failure analysis (FA). Accurate and fast FA is vital in ensuring yield, reliability, and rapid production in the semiconductor industry. However, locating defects among tens of billions of transistors packed in the tiny modern microchip is not a trivial task. Not only the process technology has to achieve such high integration of devices evolved to become astoundingly sophisticated but also debugging for defects in these chips has become remarkably complex. With electrical nanoprobing, we show how artificial intelligence-integrated physical modeling can be effective in finding difficult proverbial needle-in-a-haystack defects based on the electrical responses of the devices. Moreover, the information learned on current devices can be transferred to the latest transistor technologies, enabling machine learning-based defect sleuths of the future. Notably, we achieved a defect region classification accuracy of 99.5% on well-studied fin nanoscale field-effect transistors (FET) using a defect dataset based on an experimentally calibrated TCAD digital twin model and an adaptive boundary refinement technique. With transfer learning, a defect region classification accuracy of 99.58% is also achieved on the next-generation gate-allaround FETs, overcoming the lack of crucial datasets and optimized labels to guide and accelerate the production process of emerging devices. The proposed technique is expected to be the next level of defect identification, an important stepping stone to accelerate the production process for advanced technology nodes beyond 3 nm.

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Automated nanoprobing under scanning electron microscopy

Cited by 4 publications

References 18 publications

Automated Calibration of RF On-Wafer Probing and Evaluation of Probe Misalignment Effects Using a Desktop Micro-Factory

Automated Calibration of RF On-Wafer Probing and Evaluation of Probe Misalignment Effects Using a Desktop Micro-Factory

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM

Transfer Learning-Based Artificial Intelligence-Integrated Physical Modeling to Enable Failure Analysis for 3 Nanometer and Smaller Silicon-Based CMOS Transistors

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