A Novel Dual-Sided Fly-By Topology for 1–8 DDR With Optimized Signal Integrity by EBG Design

Chiu, Chih-Chia; Yang, Kai-Hsiang; Lin, Ying-Hsi; Wang, Wen-Shan; Wu, Ting-Ying; Wu, Ruey-Beei

doi:10.1109/tcpmt.2018.2810239

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…To present the approach sketched above, we study a simple problem class covering several common PCB routing topologies, visualized in Fig. 1, that are based on the traditional topologies also considered in Chiu et al (2018). These topologies are different simple configurations for laying components and the connection lines between them.…”

Section: Pcb Design With Various Topologiesmentioning

confidence: 99%

“…The daisy chain type of topology links multiple components together in series. A star topology can be useful in order not to impact the signal by the components as they would in a daisy chain (Chiu et al, 2018).…”

Section: Pcb Design With Various Topologiesmentioning

confidence: 99%

“…Our aim for this paper is the analysis of these chosen topologies and the minimization of reflections S 11 at the emitting port P0 as well as the maximization and equilibration of transmission parameters S n1 , 2 ≤ n ≤ 5. Following Chiu et al (2018), the receiver's load is here simply modeled as a capacitor with a capacitance of C = 11 pF. Indeed, a memory module is mainly seen as a capacitive load for the PCB's circuit, where the relevant contribution to its overall capacitance does not come from the small capacitors used to store individual bits of about 25-30 fF (Kim, 1995), but from the packaged/off-chip PCB interconnects between the DRAM memories and processors.…”

Section: Pcb Design With Various Topologiesmentioning

confidence: 99%

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Mathematical optimization and machine learning to support PCB topology identification

Cahani,

Stiemer

2023

Adv. Radio Sci.

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Abstract. In this paper, we study an identification problem for schematics with different concurring topologies. A framework is proposed, that is both supported by mathematical optimization and machine learning algorithms. Through the use of Python libraries, such as scikit-rf, which allows for the emulation of network analyzer measurements, and a physical microstrip line simulation on PCBs, data for training and testing the framework are provided. In addition to an individual treatment of the concurring topologies and subsequent comparison, a method is introduced to tackle the identification of the optimum topology directly via a standard optimization or machine learning setup: An encoder-decoder sequence is trained with schematics of different topologies, to generate a flattened representation of the rated graph representation of the considered schematics. Still containing the relevant topology information in encoded (i.e., flattened) form, the so obtained latent space representations of schematics can be used for standard optimization of machine learning processes. Using now the encoder to map schematics on latent variables or the decoder to reconstruct schematics from their latent space representation, various machine learning and optimization setups can be applied to treat the given identification task. The proposed framework is presented and validated for a small model problem comprising different circuit topologies.

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Section: Pcb Design With Various Topologiesmentioning

confidence: 99%

Section: Pcb Design With Various Topologiesmentioning

confidence: 99%

Section: Pcb Design With Various Topologiesmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical optimization and machine learning to support PCB topology identification

Cahani,

Stiemer

2023

Adv. Radio Sci.

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“…The deep neural network approach has been applied by Lu et al [204] to high-speed channel modeling for signal integrity analysis, which saves complex circuit simulations and substantial domain knowledge altogether. Chiu et al [205] have adopted ANN optimization to facilitate the design of signal integrity. Swaminathan et al [206] have discussed the neural network applications for signal and power integrity in packaging.…”

Section: E Vlsi Interconnectsmentioning

confidence: 99%

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based techniques are becoming useful for performing forward/inverse modeling for active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which are, otherwise, computationally expensive or for which efficient analytical formulas are not available. Fundamental concepts of the ANN structure and training, such as feedforward neural networks (FFNNs), recurrent neural networks (RNNs)/dynamic neural networks (DNNs)/time-delay neural networks (TDNNs), deep neural networks, and neural network training and extrapolation, are described. Knowledgebased neural networks (KBNNs) are described for improving the accuracy and reliability of modeling and design optimization. Various advanced ANN techniques, such as neuro-transfer function (neuro-TF) modeling, neural network inverse modeling, and deep neural network modeling, are discussed. The existing and emerging applications of ANN in microwave CAD are identified, such as electromagnetic (EM)/multiphysics modeling, modeling of nonlinear circuits and transistors, filter design, very largescale integration (VLSI) interconnects, oscillator, transmitter and receiver modeling, and CAD applications in such as gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), and substrate-integrated waveguide (SIW).

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Symmetric-Cell EBG Theory and Its Applications to Vias Daisy Chain for Residual Stub Detection

Wang

Huang

et al. 2022

2022 IEEE 72nd Electronic Components and Technology Conference (ECTC)

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A Novel Dual-Sided Fly-By Topology for 1–8 DDR With Optimized Signal Integrity by EBG Design

Cited by 7 publications

References 5 publications

Mathematical optimization and machine learning to support PCB topology identification

Mathematical optimization and machine learning to support PCB topology identification

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Symmetric-Cell EBG Theory and Its Applications to Vias Daisy Chain for Residual Stub Detection

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