Fu-Chun Chang

1.3k total citations
13 papers, 766 citations indexed

About

Fu-Chun Chang is a scholar working on Electrical and Electronic Engineering, Automotive Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Fu-Chun Chang has authored 13 papers receiving a total of 766 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Electrical and Electronic Engineering, 1 paper in Automotive Engineering and 1 paper in Cellular and Molecular Neuroscience. Recurrent topics in Fu-Chun Chang's work include Advanced Memory and Neural Computing (10 papers), Ferroelectric and Negative Capacitance Devices (9 papers) and Semiconductor materials and devices (8 papers). Fu-Chun Chang is often cited by papers focused on Advanced Memory and Neural Computing (10 papers), Ferroelectric and Negative Capacitance Devices (9 papers) and Semiconductor materials and devices (8 papers). Fu-Chun Chang collaborates with scholars based in Taiwan, Vietnam and China. Fu-Chun Chang's co-authors include Meng‐Fan Chang, Chuan-Jia Jhang, Je-Min Hung, Cheng-Xin Xue, Win-San Khwa, Sheng-Po Huang, Kea‐Tiong Tang, Chung‐Chuan Lo, Ren-Shuo Liu and Chih-Cheng Hsieh and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Pattern Recognition and Environmental Science and Pollution Research.

In The Last Decade

Fu-Chun Chang

13 papers receiving 750 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fu-Chun Chang Taiwan 11 687 130 109 84 84 13 766
Je-Min Hung Taiwan 12 744 1.1× 138 1.1× 123 1.1× 92 1.1× 71 0.8× 15 815
Chuan-Jia Jhang Taiwan 9 593 0.9× 118 0.9× 85 0.8× 91 1.1× 54 0.6× 14 649
Sheng-Po Huang Taiwan 7 579 0.8× 100 0.8× 64 0.6× 87 1.0× 69 0.8× 10 631
Hongwu Jiang United States 14 734 1.1× 179 1.4× 96 0.9× 134 1.6× 75 0.9× 26 819
Sujan K. Gonugondla United States 13 640 0.9× 165 1.3× 140 1.3× 49 0.6× 103 1.2× 25 737
Heiner Giefers Switzerland 10 441 0.6× 139 1.1× 107 1.0× 101 1.2× 32 0.4× 28 561
Muya Chang United States 14 392 0.6× 128 1.0× 97 0.9× 51 0.6× 60 0.7× 35 502
En-Yu Yang United States 8 494 0.7× 151 1.2× 91 0.8× 59 0.7× 131 1.6× 15 620

Countries citing papers authored by Fu-Chun Chang

Since Specialization
Citations

This map shows the geographic impact of Fu-Chun Chang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fu-Chun Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fu-Chun Chang more than expected).

Fields of papers citing papers by Fu-Chun Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fu-Chun Chang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fu-Chun Chang. The network helps show where Fu-Chun Chang may publish in the future.

Co-authorship network of co-authors of Fu-Chun Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Fu-Chun Chang. A scholar is included among the top collaborators of Fu-Chun Chang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fu-Chun Chang. Fu-Chun Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Wu, Ping-Chun, Jian-Wei Su, Yen-Lin Chung, et al.. (2023). An 8b-Precision 6T SRAM Computing-in-Memory Macro Using Time-Domain Incremental Accumulation for AI Edge Chips. IEEE Journal of Solid-State Circuits. 59(7). 2297–2309. 10 indexed citations
2.
Wen, Tai-Hao, Je-Min Hung, Hung-Hsi Hsu, et al.. (2023). A 28nm Nonvolatile AI Edge Processor using 4Mb Analog-Based Near-Memory-Compute ReRAM with 27.2 TOPS/W for Tiny AI Edge Devices. 1–2. 10 indexed citations
3.
Khwa, Win-San, Yen-Cheng Chiu, Chuan-Jia Jhang, et al.. (2022). A 40-nm, 2M-Cell, 8b-Precision, Hybrid SLC-MLC PCM Computing-in-Memory Macro with 20.5 - 65.0TOPS/W for Tiny-Al Edge Devices. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 1–3. 85 indexed citations
4.
Wu, Ping-Chun, Jian-Wei Su, Yen-Lin Chung, et al.. (2022). A 28nm 1Mb Time-Domain Computing-in-Memory 6T-SRAM Macro with a 6.6ns Latency, 1241GOPS and 37.01TOPS/W for 8b-MAC Operations for Edge-AI Devices. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 1–3. 91 indexed citations
5.
Hung, Je-Min, Tai-Hao Wen, Yen-Hsiang Huang, et al.. (2022). 8-b Precision 8-Mb ReRAM Compute-in-Memory Macro Using Direct-Current-Free Time-Domain Readout Scheme for AI Edge Devices. IEEE Journal of Solid-State Circuits. 58(1). 303–315. 32 indexed citations
6.
Hung, Je-Min, Yen-Hsiang Huang, Sheng-Po Huang, et al.. (2022). An 8-Mb DC-Current-Free Binary-to-8b Precision ReRAM Nonvolatile Computing-in-Memory Macro using Time-Space-Readout with 1286.4-21.6TOPS/W for Edge-AI Devices. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 1–3. 86 indexed citations
7.
Xue, Cheng-Xin, Je-Min Hung, Hui-Yao Kao, et al.. (2021). 16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices. 245–247. 143 indexed citations
8.
Jhang, Chuan-Jia, Cheng-Xin Xue, Je-Min Hung, Fu-Chun Chang, & Meng‐Fan Chang. (2021). Challenges and Trends of SRAM-Based Computing-In-Memory for AI Edge Devices. IEEE Transactions on Circuits and Systems I Regular Papers. 68(5). 1773–1786. 170 indexed citations
9.
Hung, Je-Min, Cheng-Xin Xue, Hui-Yao Kao, et al.. (2021). A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices. Nature Electronics. 4(12). 921–930. 86 indexed citations
10.
11.
Chang, Fu-Chun & Yi-Pin Lin. (2019). Survey of lead concentration in tap water on a university campus. Environmental Science and Pollution Research. 26(24). 25275–25285. 18 indexed citations
12.
Hsu, Tzu-Hsiang, Yen-Kai Chen, Wei-Chen Wei, et al.. (2019). A 0.5V Real-Time Computational CMOS Image Sensor with Programmable Kernel for Always-On Feature Extraction. 33–34. 17 indexed citations
13.
Shih, Frank Y., et al.. (1992). A new art-based neural architecture for pattern classification and image enhancement without prior knowledge. Pattern Recognition. 25(5). 533–542. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026