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Siying Zheng

dblp:423/0039 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Memory systems · 41% Hardware accelerators and domain-specific architectures · 18% Emerging computing paradigms · 14%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Integrated circuit design › emerging device technologies
ferroelectric devices
0.912025
Ferroelectric materials, devices, and chips technologies for advanced computing and memory applications: development and challenges · Sci. China Inf. Sci. 2025
Memory systems › non-volatile memory
ferroelectric memory
0.912025
Ferroelectric materials, devices, and chips technologies for advanced computing and memory applications: development and challenges · Sci. China Inf. Sci. 2025
Hardware accelerators and domain-specific architectures › machine learning accelerator
in-memory computing accelerator
0.912025
A parallel computing-in-memory accelerator utilizing FeRAM array with retention loss correction · Sci. China Inf. Sci. 2025
Emerging computing paradigms
neuromorphic computing
0.912025
Ferroelectric materials, devices, and chips technologies for advanced computing and memory applications: development and challenges · Sci. China Inf. Sci. 2025
Memory systems
non-volatile memory
0.912025
Ferroelectric materials, devices, and chips technologies for advanced computing and memory applications: development and challenges · Sci. China Inf. Sci. 2025
Memory systems
processing-in-memory
0.912025
A parallel computing-in-memory accelerator utilizing FeRAM array with retention loss correction · Sci. China Inf. Sci. 2025

Methods — techniques the papers use, named apart from their topics

retention loss correction · 0.9ferroelectric materials engineering · 0.9FeRAM · 0.9
YearPublicationVenuePosition
2025 A parallel computing-in-memory accelerator utilizing FeRAM array with retention loss correction
Wei Mao 0002, Bo Li 0155, Xiaomeng Lv, Fuyi Li, Haiqiao Hong, Shirui Zhao, Siying Zheng, Jiuren Zhou, Yan Liu 0016, Genquan Han
Sci. China Inf. Sci.10
2025 Ferroelectric materials, devices, and chips technologies for advanced computing and memory applications: development and challenges
abstract
Abstract Hafnium (Hf) oxide-based ferroelectric materials have emerged as a transformative platform for next-generation non-volatile memory and advanced computing technologies. This review comprehensively examines the development, challenges, and applications of HfO 2 ferroelectrics, emphasizing their CMOS compatibility, scalability, and robust polarization at nanoscale dimensions. Breakthroughs in doping strategies, stress engineering, and VO control have stabilized the metastable orthorhombic phase, enabling high-performance devices such as ferroelectric RAM (FeRAM), ferroelectric field-effect transistors (FeFETs), and ferroelectric tunnel junctions (FTJs). These devices offer ultrafast switching, low power consumption, and multi-level storage, driving innovations in neuromorphic computing, in-memory processing, and cryogenic systems; nonetheless, they face ongoing challenges in reliability, such as fatigue and imprint effects, and scalability at sub-5 nm technology nodes. Emerging frontiers, such as wurtzite-structured nitrides (e.g., AlScN) and antiferroelectric ZrO 2 -based systems, have garnered significant attention due to their exceptionally high remanent polarization and promising potential for enhanced endurance, respectively. Further addressing the reliability issues of these emerging ferroelectric materials and the challenges associated with large-scale integration processes through interdisciplinary efforts will unlock the full potential of ferroelectric technologies, positioning them as pivotal enablers of post-Moore computing architectures and sustainable AI-driven applications.
Ni Zhong, Tianjiao Xin, Tiancheng Gong, Jiezhi Chen, Zhiyuan Fu, Kechao Tang, Xiuyan Li, Xinqiang Wang, Anquan Jiang, Peiyuan Du, Chengji Jin, Haoji Qian, Siying Zheng, Haiwen Xu, Bochang Li, Zheng-Dong Luo, Jiuren Zhou, Genquan Han
Sci. China Inf. Sci.29