Archives: AI in Wave division

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In this article, we present a novel power noise induced eye diagram estimation method for high-speed channel design with generative adversarial learning. By leveraging a tailored adaptive Gramian-Angular-Field segmentation integration (AGSI) framework with generative adversarial networks (GANs), the proposed
AGSI–GAN accurately and efficiently estimates eye diagrams under challenging design scenarios involving signal integrity (SI) and power integrity (PI) interactions, such as crosstalk and switching noise. Our approach, AGSI–GAN, enhances the U-Net generator’s learning efficiency by utilizing AGSI as condition images that encapsulate domain characteristics related to SI/PI. By integrating single-bit response, far-end crosstalk and simultaneous switching
noise as modular components, AGSI converts this integrated data into images to enable both the generator and the discriminator interpret condition images with metafeatures. The trained AGSI–GAN reduces runtime by 88.6% compared to full-transient simulation, while maintaining high accuracy with all eye diagram metrics, including cumulative areas, showing average mean absolute percentage errors below 3% . Furthermore, AGSI–GAN facilitates
accelerated design optimization while enabling the estimation of complete eye diagrams for the defined objective function, effectively addressing complex SI/PI tradeoffs. The framework shows significant potential for expediting the optimization process, integrating.

 

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It has long been desired to enable global structural optimization of organic light-emitting diodes (OLEDs) for maximal light extraction. The most critical obstacles to achieving this goal are time-consuming optical simulations and discrepancies between simulation and experiment. In this work, by leveraging transfer learning, we demonstrate that fast and reliable prediction of OLED optical properties is possible with several times higher data efficiency compared to previously demonstrated surrogate solvers based on artificial neural networks. Once a neural network is trained for a base OLED structure, it can be transferred to predict the properties of modified structures with additional layers with a relatively small number of additional training samples. Moreover, we demonstrate that, with only a few tenths of experimental data sets, a neural network can be trained to accurately predict experimental measurements of OLEDs, which often differ from simulation results due to fabrication and measurement errors. This is enabled by transferring a pre-trained network, built with a large amount of simulated data, to a new network capable of correcting systematic errors in experiment. Our work proposes a practical approach to designing and optimizing OLED structures with a large number of design parameters to achieve high optical efficiency.

 

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[Prof. Joungho Kim, Hyunwook Park, from left]

 

-Award Name: Best Poster Award

-Paper Title: Scalable Transformer Network-based Reinforcement Learning Method for PSIJ Optimization in HBM

-Authors: Hyunwook Park, Taein Shin, Seongguk Kim, Daehwan Lho, Boogyo Sim, Jinouk Song, Kyu-Bong, and Joungho Kim (Corresponding author) 

-Conference Name: 2022 IEEE 31th Conference on Electrical Performance of Electronic Packaging and Systems

-Time of the event: 9 to 12^th October, 2022 at San Jose, CA, USA

 

KAIST EE Postdoc researcher Hyunwook Park (under the supervision of Professor Joungho Kim) won the Best Poster Award at 2022 IEEE 31th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS Conference), which was held at San Jose, California, from 9 to 12^th October.  

 

EPEPS Conference is an annual academic conference in which many prestigious universities and companies share their research works in the field of signal and power integrity-based semiconductor.

 

Postdoc researcher Researcher Hyunwook Park presented the paper “Scalable Transformer Network-based Reinforcement Learning Method for PSIJ Optimization in HBM”, which was nominated for the Best Poster Award thanks to its excellence.

 

KAIST EE Prof. Jang, Min Seok and his research team succeed in observing strongly confined mid-infrared light propagating on monocrystalline gold with a scattering-type scanning near-field optical microscope (s-SNOM). 

It is highly applicable in next-generation optoelectronic devices development, with increasing the interaction between light and matter by confining the light in an atomically flat nanostructure. The study will be useful in advancing high-efficient nanophotonics and quantum computing.

 

[From left, Prof. Jang, Min Seok and Research Prof. Sergey Menabde]

 

KAIST announced on the July 18th that a joint research effort has succeeded in implementing a new platform for strongly confined light propagation in a low-dimensional material. This finding is expected to contribute to the development of next-generation optoelectronic devices development based on strong light-matter interactions.

 

Stacking atomically flat two-dimensional materials results in van der Waals crystals, which exhibit properties different from the original two-dimensional materials. Phonon-polaritons are the composite quasi-particles resulting from the polar dielectric ions’ oscillations coupling with electromagnetic waves. In particular, phonon-polaritons forming in van der Waals crystals placed on highly conductive metals have a high degree of compression. This is because the charges in the polariton crystals reflect in the underneath metal due to the image charge effect, and thus produce a new kind of polariton called image phonon-polaritons.

 

Light propagating as image phonon-polaritons may induce strong light-matter interactions, but their propagation is limited on rough metal surfaces and thus suffers low feasibility.

 

Prof. Jang said, “This work illustrates very well the advantages of image polaritons, especially image phonon-polaritons. Image phonon-polaritons exhibit low loss and strong light-matter interactions useful in the development of next-generation optoelectronic devices.” He then added that he hopes to advance the commercialization of high-efficiency nanophotonic devices, including in metasurfaces, optical switches, and optical sensors.

 

This work, first-authored by Research Prof. Sergey Menabde, was published in Science Advances on the July 13th. It has been supported by Samsung Science & Technology Foundation and the National Research Foundation of Korea, as well as Korea Institute of Science and Technology, Japanese Ministry of Education, Culture, Sports, Science and Technology, and the Villum Foundation of Denmark.

 

 

Fig. 1 Nanotip used in measuring the image phonon-polaritons traveling in h-BN in super-high resolution