Chinese semiconductor thread II

tokenanalyst

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Xinlu Technology (RapidFlex) received tens of millions of yuan in angel rounds and is an FPGA chip design company.​

Recently, Pudong Technology Investment Angel Fund of Funds, a subsidiary of Pudong Venture Capital, participated in the completion of an angel round investment of tens of millions yuan in FPGA chip design company Shanghai Xinlu Technology Co., Ltd. (hereinafter referred to as "Xinlu Technology").

It is reported that Xinlu Technology is a business-driven design service provider of embedded FPGA chips and programmable SoC (PSoC) chips. It has both EDA software and FPGA hardware R&D capabilities and is committed to providing services to various industries in the industrial, consumer, communications and automotive industries. Embedded FPGA (eFPGA) solutions are provided for various applications.

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tphuang

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Looks like TechInsights is going to make a teardown of the Huawei Pura smartphone as usual.
this will be interesting. Chinese online videos claim P70 is using CXMT and YMTC memory chips. Now, we know CXMT just started LPDDR5 production. So if CXMT DRAMs do end up on P70, that's quite an interesting development
 

tokenanalyst

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This can be fabricated using an standard semiconductor manufacturing process

The First In The World! University Of Electronic Science And Technology Of China Team Develops Gallium Nitride Quantum Light Source Chip​

Reporters learned from the Information and Quantum Laboratory of the University of Electronic Science and Technology of China that the laboratory's research team cooperated with Tsinghua University and the Shanghai Institute of Microsystems and Information Technology of the Chinese Academy of Sciences to develop a gallium nitride quantum light source chip for the first time in the world. This is another important progress made by the University of Electronic Science and Technology of China's "Ginkgo-1" urban quantum Internet research platform. The relevant results were recently published in "Physical Review Letters".

It is understood that the quantum light source chip is the core device of the quantum Internet. It can be regarded as a "quantum light bulb" that lights up the "quantum room", allowing Internet users to have the ability to interact with quantum information.
Through iterative electron beam exposure and dry etching processes, the research team overcame technical problems such as high-quality gallium nitride crystal film growth, waveguide sidewall and surface scattering losses, and applied gallium nitride materials to quantum light source chips for the first time in the world. .

At present, quantum light source chips are mostly developed using materials such as silicon nitride. In contrast, the output wavelength range of gallium nitride quantum light source chips has increased from 25.6 nanometers to 100 nanometers, and can be developed towards monolithic integration.

"This means that the 'quantum light bulb' can light up more rooms." Zhou Qiang, professor at the Institute of Basic and Frontier Research at the University of Electronic Science and Technology of China and director of the Quantum Internet Frontier Research Center at Tianfu Jiangxi Laboratory, explained that more wavelength resources can enable more Users use different wavelengths to access the quantum Internet network.

Not long ago, the team increased the capacity of solid-state quantum storage in the optical fiber communication band to 1,650 modes, breaking the world record in this field. A series of research progress will further provide key components for the construction of large-capacity, long-distance, and high-fidelity quantum Internet.

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tokenanalyst

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Machine Learning Applied to Electron Beam Lithography to Accelerate Process Optimization of a Contact Hole Layer​

Abstract​

Determining the lithographic process conditions with high-resolution patterning plays a crucial role in accelerating chip manufacturing. However, lithography imaging is an extremely complex nonlinear system, and obtaining suitable process conditions requires extensive experimental attempts. This severely creates a bottleneck in optimizing and controlling the lithographic process conditions. Herein, we report a process optimization solution for a contact layer of metal oxide nanoparticle photoresists by combining electron beam lithography (EBL) experiments with machine learning. In this solution, a long short-term memory (LSTM) network and a support vector machine (SVM) model are used to establish the contact hole imaging and process condition classification models, respectively. By combining SVM with the LSTM network, the process conditions that simultaneously satisfy the requirements of the contact hole width and local critical dimension uniformity tolerance can be screened. The verification results demonstrate that the horizontal and vertical contact widths predicted by the LSTM network are highly consistent with the EBL experimental results, and the classification model shows good accuracy, providing a reference for process optimization of a contact layer.​

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