TY  - THES
A1  - Grum, Marcus
T1  - Construction of a concept of neuronal modeling
N2  - The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
N2  - Die vorliegende Arbeit addressiert das Geschäftsproblem von ineffizienten Prozessen, unpräzisen Prozessanalysen und -simulationen sowie untransparenten künstlichen neuronalen Netzwerken, indem ein Modellierungskonzept zum Neuronalen Modellieren konstruiert wird. Dieses neuartige Konzept des Neuronalen Modellierens (CoNM) fungiert als flexibler und effizienter Ansatz zum Modellieren, Simulieren und Optimieren von Prozessen mit Hilfe von neuronalen Netzwerken und wird mittels einer Modellierungssprache, dessen mathematischen Formalisierung und technischen Substanziierung sowie einer Sammlung von neuartigen Subartefakten beschrieben. In der Verwendung derer Implementierung als CoNM-Werkzeuge können somit neue Arten einer Neuronalen-Prozess-Modellierung (NPM), Neuronalen-Prozess-Simulation (NPS) sowie Neuronalen-Prozess-Optimierung (NPO) realisiert werden. Die Wirksamkeit der erstellten Artefakte wurde anhand von sechs Experimenten demonstriert sowie in einem Simulator in realen Produktionsprozessen gezeigt.
T2  - Konzept des Neuronalen Modellierens
KW  - Deep Learning
KW  - Artificial Neuronal Network
KW  - Explainability
KW  - Interpretability
KW  - Business Process
KW  - Simulation
KW  - Optimization
KW  - Knowledge Management
KW  - Process Management
KW  - Modeling
KW  - Process
KW  - Knowledge
KW  - Learning
KW  - Enterprise Architecture
KW  - Industry 4.0
KW  - Künstliche Neuronale Netzwerke
KW  - Erklärbarkeit
KW  - Interpretierbarkeit
KW  - Geschäftsprozess
KW  - Simulation
KW  - Optimierung
KW  - Wissensmanagement
KW  - Prozessmanagement
KW  - Modellierung
KW  - Prozess
KW  - Wissen
KW  - Lernen
KW  - Enterprise Architecture
KW  - Industrie 4.0
Y1  - 2021
ER  - 
TY  - JOUR
A1  - Li, Yuanqing
A1  - Chen, Li
A1  - Nofal, Issam
A1  - Chen, Mo
A1  - Wang, Haibin
A1  - Liu, Rui
A1  - Chen, Qingyu
A1  - Krstić, Miloš
A1  - Shi, Shuting
A1  - Guo, Gang
A1  - Baeg, Sang H.
A1  - Wen, Shi-Jie
A1  - Wong, Richard
T1  - Modeling and analysis of single-event transient sensitivity of a 65 nm clock tree
JF  - Microelectronics reliability
N2  - The soft error rate (SER) due to heavy-ion irradiation of a clock tree is investigated in this paper. A method for clock tree SER prediction is developed, which employs a dedicated soft error analysis tool to characterize the single-event transient (SET) sensitivities of clock inverters and other commercial tools to calculate the SER through fault-injection simulations. A test circuit including a flip-flop chain and clock tree in a 65 nm CMOS technology is developed through the automatic ASIC design flow. This circuit is analyzed with the developed method to calculate its clock tree SER. In addition, this circuit is implemented in a 65 nm test chip and irradiated by heavy ions to measure its SER resulting from the SETs in the clock tree. The experimental and calculation results of this case study present good correlation, which verifies the effectiveness of the developed method.
KW  - Clock tree
KW  - Modeling
KW  - Single-event transient (SET)
Y1  - 2018
U6  - https://doi.org/10.1016/j.microrel.2018.05.016
SN  - 0026-2714
VL  - 87
SP  - 24
EP  - 32
PB  - Elsevier
CY  - Oxford
ER  -