Fluttering Pattern Generation Using Modified Legendre Sequence for Coded Exposure Imaging

Author(s):  
Hae-Gon Jeon ◽  
Joon-Young Lee ◽  
Yudeog Han ◽  
Seon Joo Kim ◽  
In So Kweon
2021 ◽  
Vol 139 ◽  
pp. 106489
Author(s):  
Guangmang Cui ◽  
Xiaojie Ye ◽  
Jufeng Zhao ◽  
Liyao Zhu ◽  
Ying Chen ◽  
...  

2003 ◽  
Vol 766 ◽  
Author(s):  
Vineet Sharma ◽  
Arief B. Suriadi ◽  
Frank Berauer ◽  
Laurie S. Mittelstadt

AbstractNormal photolithography tools have focal depth limitations and are unable to meet the expectations of high resolution photolithography on highly topographic structures. This paper shows a cost effective and promising technique of combining two different approaches to achieve critical dimensions of traces on slope pattern continuity on highly topographic structures. Electrophoretically deposited photoresist is used on 3-D structured wafers. This photoresist coating technique is fairly known in the MEMS industries to achieve uniform and conformal photoresist films on 3D surfaces. Multi step exposures are used to expose electrophoretically deposited photoresist. AlCu (Cu-0.5%), 0.47-0.53 μm thick metal film is deposited on 3D structured silicon substrate to plate photoresist. By combining these two novel methods, metal (AlCu) traces of 75 μm line width and 150 μm pitch (from top flat to down the slope) have been demonstrated on isotropically etched 350 μm deep trenches with 5-10% line width loss.


Author(s):  
Rudolf Schlangen ◽  
Jon Colburn ◽  
Joe Sarmiento ◽  
Bala Tarun Nelapatla ◽  
Puneet Gupta

Abstract Driven by the need for higher test-compression, increasingly many chip-makers are adopting new DFT architectures such as “Extreme-Compression” (XTR, supported by Synopsys) with on-chip pattern generation and MISR based compression of chain output data. This paper discusses test-loop requirements in general and gives Advantest 93k specific guidelines on test-pattern release and ATE setup necessary to enable the most established EFA techniques such as LVP and SDL (aka DLS, LADA) within the XTR test architecture.


Author(s):  
Rommel Estores ◽  
Karo Vander Gucht

Abstract This paper discusses a creative manual diagnosis approach, a complementary technique that provides the possibility to extend Automatic Test Pattern Generation (ATPG) beyond its own limits. The authors will discuss this approach in detail using an actual case – a test coverage issue where user-generated ATPG patterns and the resulting ATPG diagnosis isolated the fault to a small part of the digital core. However, traditional fault localization techniques was unable to isolate the fault further. Using the defect candidates from ATPG diagnosis as a starting point, manual diagnosis through fault Injection and fault simulation was performed. Further fault localization was performed using the ‘not detected’ (ND) and/or ‘detected’ (DT) fault classes for each of the available patterns. The result has successfully deduced the defect candidates until the exact faulty net causing the electrical failure was identified. The ability of the FA lab to maximize the use of ATPG in combination with other tools/techniques to investigate failures in detail; is crucial in the fast root cause determination and, in case of a test coverage, aid in having effective test screen method implemented.


Author(s):  
Astrid A. Prinz

This chapter begins by defining central pattern generators (CPGs) and proceeds to focus on one of their core components, the timing circuit. After arguing why invertebrate CPGs are particularly useful for the study of neuronal circuit operation in general, the bulk of the chapter then describes basic mechanisms of CPG operation at the cellular, synaptic, and network levels, and how different CPGs combine these mechanisms in various ways. Finally, the chapter takes a semihistorical perspective to discuss whether or not the study of invertebrate CPGs has seen its prime and what it has contributed—and may continue to offer—to a wider understanding of neuronal circuits in general.


2002 ◽  
Vol 38 (8) ◽  
pp. 376 ◽  
Author(s):  
Zhang Guohua ◽  
Zhou Quan
Keyword(s):  

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