scholarly journals Author Correction: Photodetection Characteristics of Gold Coated AFM Tips and n-Silicon Substrate nano-Schottky Interfaces

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yawar Abbas ◽  
Ayman Rezk ◽  
Fatmah Alkindi ◽  
Irfan Saadat ◽  
Ammar Nayfeh ◽  
...  
Keyword(s):  
Silicon Substrate ◽  
Afm Tips

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

InGaAs/Si Heterojunction Tunneling Field-Effect Transistor on Silicon Substrate

2014 ◽  
Vol E97.C (7) ◽  
pp. 677-682
Author(s):  
Sung YUN WOO ◽  
Young JUN YOON ◽  
Jae HWA SEO ◽  
Gwan MIN YOO ◽  
Seongjae CHO ◽  
...  
Keyword(s):  
Silicon Substrate ◽  
Field Effect ◽  
Field Effect Transistor ◽  
Effect Transistor

Transport Properties Of Metallic Nanowires On Silicon Substrate

2014 ◽  
Vol 2 (1) ◽  
pp. 20-23
Author(s):  
Jaskiran Kaur ◽  
◽  
Surinder Singh ◽  
Keyword(s):  
Transport Properties ◽  
Silicon Substrate ◽  
Metallic Nanowires

Process Induced Defects in the Silicon Substrate: Approaches for Successful Failure Analysis

2010 ◽  
Author(s):  
J.G. van Hassel ◽  
Xiao-Mei Zhang
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Silicon Substrate ◽  
Processing Technique ◽  
Electrical Characteristics ◽  
Localization Method ◽  
The Right ◽  
Induced Defects

Abstract Failures induced in the silicon substrate by process marginalities or process mistakes need continuous attention in new as well as established technologies. Several case studies showing implant related defects and dislocations in silicon will be discussed. Depending on the electrical characteristics of the failure the localization method has to be chosen. The emphasis of the discussion will be on the importance of the right choice for further physical de-processing to reveal the defect. This paper focuses on the localization method, the de- processing technique and the use of Wright etch for subsequent TEM preparation.

Studies on a Novel Qualification Method of Silicon Nitride Layer

2016 ◽  
Author(s):  
Younan Hua ◽  
Bingsheng Khoo ◽  
Henry Leong ◽  
Yixin Chen ◽  
Eason Chan ◽  
...  
Keyword(s):  
Silicon Nitride ◽  
Silicon Substrate ◽  
Nitride Layer ◽  
Passivation Layer ◽  
Wafer Fabrication ◽  
Wafer Surface ◽  
Si3n4 Layer ◽  
Silicon Nitride Layer ◽  
Passivation Layers ◽  
Pinhole Test

Abstract In wafer fabrication, a silicon nitride (Si3N4) layer is widely used as passivation layer. To qualify the passivation layers, traditionally chemical recipe PAE (H3PO4+ HNO3) is used to conduct passivation pinhole test. However, it is very challenging for us to identify any pinholes in the Si3N4 layer with different layers underneath. For example, in this study, the wafer surface is Si3N4 layer and the underneath layer is silicon substrate. The traditional receipt of PAE cannot be used for passivation qualification. In this paper, we will report a new recipe using KOH solution to identify the pinhole in the Si3N4 passivation layer.

FIB Micromachining and Nano-Structure Fabrication

1999 ◽  
Author(s):  
Raymond A. Lee ◽  
Patrick J. Wolpert
Keyword(s):  
High Precision ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Biological Applications ◽  
Optical Lens ◽  
Nano Structure ◽  
Beam System ◽  
Afm Tips ◽  
Established Technique ◽  
Made In

Abstract FIB Micromachining has long been an established technique, but until recently it has been overshadowed by the more mainstream semiconductor application of the Focused Ion Beam system. Nano- Structure fabrication using the FIB system has become more popular recently due to several factors. The need for sub-micron structures have grown significantly due to a need for enhanced optical and biological applications. Another reason for the growth in micromachining is the improvement made in the ability of FIB systems to produce geometric shapes with high precision. With the latest high-end FIB systems, it is possible to produce microstructures with tens of nano-meters of precision. Optical lens, AFM tips, and nano-apertures are all part of the growing application for FIB Micromachining. This paper will discuss the ability and limitations of the FIB system and some possible application for FIB Micromachining.

DNA Hybridization on Chitosan-Functionalized Silicon Substrate

2016 ◽  
Vol 12 (5) ◽  
pp. 464-471 ◽  
Author(s):  
Amina Omar ◽  
El-Sayed M. El-Sayed ◽  
Mona S. Talaat ◽  
Medhat Ibrahim
Keyword(s):  
Silicon Substrate ◽  
Dna Hybridization

scholarly journals Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 772
Author(s):  
Seunghyun Kim ◽  
Osung Kwon ◽  
Hojeong Ryu ◽  
Sungjun Kim
Keyword(s):  
Resistive Switching ◽  
Silicon Substrate ◽  
Photoelectron Spectroscopy ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Alloy Type ◽  
Switching Type ◽  
Neuromorphic System ◽  
Device Properties ◽  
Partial Reset

This work demonstrates the synaptic properties of the alloy-type resistive random-access memory (RRAM). We fabricated the HfAlOx-based RRAM for a synaptic device in a neuromorphic system. The deposition of the HfAlOx film on the silicon substrate was verified by X-ray photoelectron spectroscopy (XPS) analysis. It was found that both abrupt and gradual resistive switching could be implemented, depending on the reset stop voltage. In the reset process, the current gradually decreased at weak voltage, and at strong voltage, it tended to decrease rapidly by Joule heating. The type of switching determined by the first reset process was subsequently demonstrated to be stable switching by successive set and reset processes. A gradual switching type has a much smaller on/off window than abrupt switching. In addition, retention maintained stability up to 2000 s in both switching cases. Next, the multiple current states were tested in the gradual switching case by identical pulses. Finally, we demonstrated the potentiation and depression of the Cu/HfAlOx/Si device as a synapse in an artificial neural network and confirmed that gradual resistive switching was suitable for artificial synapses, using neuromorphic system simulation.

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