An adaptive model for the control of critical dimension in photolithography process

For a submicron photolithography process, there is little room for error. In this paper, an optimized technique for photoresist (PR) development is reported, to fabricate a nanogap biosensor for application in biomedical nanodiagnostics. The pattern transfer on the wafer substrate requires precise alignment and Deep Ultra-Violet (DUV) light exposure. This research describes the photolithography process to develop a standard manufacturing procedure for pattern transfer from chrome mask. The key factor for PR development is understood and the optimization is done based on the PR thickness, spin speed, spin time, exposure time, post-exposure bake (PEB) time, developer concentration and developing time to achieve the design feature size of 1 micron. The PR is coated and spun at 3000 rpm and 5000 rpm at 30s and 40s respectively to form a very thin layer. However, the UV exposure time is remained constant at 10s. After the pattern transfer, the wafer is immersed in different concentrations of RD6 developer to develop the PR. To further improve the resolution of image transfer, the PEB time is also optimized for a better throughput on feature size. These optimizations are important to reduce the dimension error and were able to achieve error free design to protect critical dimension and prevent device failure.

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Application of Deep Neural Network in the Design of Microresonators Subject to Microfabrication Constraints

Volume 13: Micro- and Nano-Systems Engineering and Packaging ◽

10.1115/imece2020-24408 ◽

2020 ◽

Author(s):

Seyedhamidreza Alaie

Keyword(s):

Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Design Space ◽

Natural Frequencies ◽

Critical Dimension ◽

Free Standing ◽

Element Analysis ◽

Photolithography Process ◽

Vibrational Features

Abstract Deep Neural Networks (DNNs) have transformed various fields, yet their application in design of microsystem has been limited. Here, we demonstrate a framework for application of adversarial generative networks in design of microresonators targeting for operation in MHz frequencies. The topology of the microresonators are constrained based on the critical dimension common in the photolithography process. The design-space includes a free standing window of Si film on an SOI wafer that is supported on its four sides. The film is etched through a mask with an arbitrary 2D layout. We employ DNNs in finding 2D layouts that result in a membrane with desirable natural frequencies. For this purpose, deep generative networks (generators) and deep neural networks (discriminators) are employed. Two datasets representing desirable and undesirable layouts are created using finite element analysis. Subsequently, the discriminator is trained to classify desirable and undesirable layouts as well as those generated by the generator. Simultaneously, the generator is trained to sample layouts that are more likely to be classified as desirable by the discriminator. The results show that this framework is capable of producing designs with desirable vibrational features, encouraging further research in this topic.

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Information capacity and bandwidth limitations in the SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100152392 ◽

1989 ◽

Vol 47 ◽

pp. 84-85

Author(s):

D. C. Joy ◽

R. D. Bunn

Keyword(s):

Signal To Noise Ratio ◽

Critical Dimension ◽

Information Capacity ◽

Acquisition Time ◽

Signal To Noise ◽

Sem Image ◽

Probe Size ◽

Minimum Number ◽

Noise Ratio ◽

Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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Competitive Capacity and Modernization of the Russian Economy

Voprosy Ekonomiki ◽

10.32609/0042-8736-2004-7-4-34 ◽

2004 ◽

pp. 4-34 ◽

Cited By ~ 1

Author(s):

E. Yasin ◽

A. Yakovlev

Keyword(s):

Present State ◽

Transition Economy ◽

Adaptive Model ◽

Russian Economy

Having analyzed the present state of the Russian economy the authors come to the conclusion that the only reasonable goal of its modernization is achieving high competitive capacity of production. External and internal competitive capacity is analysed in detail basing on broad statistics as well as competitive capacity of institutions and their changes, the adaptive model of transition economy. According to the authors implementation of competitive capacity policy as a national idea should take into account long-term perspective.

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Analysis of Retention Time Degradation Caused by Electrostatic Potential Variation between Adjacent Bit-Line and Adjacent Contact Node in DRAM

ISTFA 2016: Conference Proceedings from the 42nd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2016p0091 ◽

2016 ◽

Author(s):

Jungil Mok ◽

Byungki Kang ◽

Daesun Kim ◽

Hongsun Hwang ◽

Sangjae Rhee ◽

...

Keyword(s):

Electric Field ◽

Electrostatic Potential ◽

Critical Dimension ◽

Data Retention ◽

Signal Line ◽

Adjacent Node ◽

Storage Node ◽

Retention Characteristics ◽

Node Voltage ◽

The Relationship

Abstract Systematic retention failure related on the adjacent electrostatic potential is studied with sub 20nm DRAM. Unlike traditional retention failures which are caused by gate induced drain leakage or junction leakage, this failure is influenced by the combination of adjacent signal line and adjacent contact node voltage. As the critical dimension between adjacent active and the adjacent signal line and contact node is scaled down, the effect of electric field caused by adjacent node on storage node is increased gradually. In this paper, we will show that the relationship between the combination electric field of adjacent nodes and the data retention characteristics and we will demonstrate the mechanism based on the electrical analysis and 3D TCAD simulation simultaneously.

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TEM Sample Preparation by Single-Sided Low-Energy Ion Beam Etching

ISTFA 2011: Conference Proceedings from the 37th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2011p0305 ◽

2011 ◽

Author(s):

Liew Kaeng Nan ◽

Lee Meng Lung

Keyword(s):

Sample Preparation ◽

Semiconductor Device ◽

Ion Beam ◽

Critical Dimension ◽

Low Energy ◽

Ex Situ ◽

Good Image Quality ◽

Ion Beam Etching ◽

Device Structures ◽

Common Technique

Abstract Conventional FIB ex-situ lift-out is the most common technique for TEM sample preparation. However, the scaling of semiconductor device structures poses great challenge to the method since the critical dimension of device becomes smaller than normal TEM sample thickness. In this paper, a technique combining 30 keV FIB milling and 3 keV ion beam etching is introduced to prepare the TEM specimen. It can be used by existing FIBs that are not equipped with low-energy ion beam. By this method, the overlapping pattern can be eliminated while maintaining good image quality.

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