Michael Niemier

5.5k total citations · 1 hit paper
189 papers, 4.0k citations indexed

About

Michael Niemier is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Michael Niemier has authored 189 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 161 papers in Electrical and Electronic Engineering, 61 papers in Atomic and Molecular Physics, and Optics and 56 papers in Computational Theory and Mathematics. Recurrent topics in Michael Niemier's work include Advanced Memory and Neural Computing (109 papers), Ferroelectric and Negative Capacitance Devices (86 papers) and Semiconductor materials and devices (56 papers). Michael Niemier is often cited by papers focused on Advanced Memory and Neural Computing (109 papers), Ferroelectric and Negative Capacitance Devices (86 papers) and Semiconductor materials and devices (56 papers). Michael Niemier collaborates with scholars based in United States, China and Germany. Michael Niemier's co-authors include Xiaobo Sharon Hu, Xunzhao Yin, Peter M. Kogge, Wolfgang Porod, Gary H. Bernstein, Dayane Reis, Ann Franchesca Laguna, Suman Datta, Kai Ni and Joseph Nahas and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Journal of Applied Physics.

In The Last Decade

Michael Niemier

180 papers receiving 3.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Ferroelectric ternary content-addressable memory for one-shot learning

2019 282 citations Kai Ni, Xunzhao Yin et al. profile →

Peers

Michael Niemier

EEE

CTM

AMPO

AI

MC

György Csaba United States

Takahiro Hanyu Japan

Mohammad Hossein Moaiyeri Iran

Keivan Navi Iran

Mehdi B. Tahoori Germany

Shaahin Angizi United States

Ralph K. Cavin United States

Mark Anders United States

Swaroop Ghosh United States

Sorin Cotöfană Netherlands

György Csaba United States

Michael Niemier

2.9k ×0.9

EEE

1.2k ×1.0

CTM

2.7k ×2.7

AMPO

569 ×1.0

AI

405 ×1.0

MC

Citations per year, relative to Michael Niemier Michael Niemier (= 1×) peers György Csaba

Countries citing papers authored by Michael Niemier

Since Specialization

Citations

This map shows the geographic impact of Michael Niemier'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 Michael Niemier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Niemier more than expected).

Fields of papers citing papers by Michael Niemier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Niemier. 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 Michael Niemier. The network helps show where Michael Niemier may publish in the future.

Co-authorship network of co-authors of Michael Niemier

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Niemier. A scholar is included among the top collaborators of Michael Niemier 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 Michael Niemier. Michael Niemier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Geng, Haoran, et al.. (2025). Shared-PIM: Enabling Concurrent Computation and Data Flow for Faster Processing-in-DRAM. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(11). 4395–4404.

2.

Hu, Xiaobo Sharon, et al.. (2025). Cross-Layer Design and Design Automation for In-Memory Computing Based on Nonvolatile Memory Technologies. IEEE Design and Test. 42(6). 75–86.

3.

Ni, Kai, Wriddhi Chakraborty, Yogesh Singh Chauhan, et al.. (2024). A Physics-Based Model for Oxide–Semiconductor-Based Ferroelectric Field-Effect Transistors. IEEE Transactions on Electron Devices. 71(7). 4397–4402. 5 indexed citations

4.

Yin, Xunzhao, Franz Müller, Ann Franchesca Laguna, et al.. (2024). Deep random forest with ferroelectric analog content addressable memory. Science Advances. 10(23). eadk8471–eadk8471. 13 indexed citations

5.

Kazemi, Arman, Ann Franchesca Laguna, Rui Lin, et al.. (2022). Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search. Nature Communications. 13(1). 6284–6284. 43 indexed citations

6.

Reis, Dayane, et al.. (2022). Ferroelectric FET Configurable Memory Arrays and Their Applications. 2022 International Electron Devices Meeting (IEDM). 21.5.1–21.5.4. 2 indexed citations

7.

Reis, Dayane, Michael Niemier, & Xiaobo Sharon Hu. (2021). The Implications of Ferroelectric FET Device Models to the Design of Computing-in-Memory Architectures. Journal of Integrated Circuits and Systems. 16(1). 1–8. 2 indexed citations

8.

Laguna, Ann Franchesca, Michael Niemier, & Xiaobo Sharon Hu. (2019). Design of Hardware-Friendly Memory Enhanced Neural Networks. 1583–1586. 21 indexed citations

9.

Nahas, Joseph, et al.. (2015). Analytically Modeling Power and Performance of a CNN System. International Conference on Computer Aided Design. 186–193. 2 indexed citations

10.

Sedighi, Behnam, et al.. (2015). A CNN-inspired mixed signal processor based on tunnel transistors. Design, Automation, and Test in Europe. 1150–1155. 2 indexed citations

11.

Niemier, Michael, et al.. (2014). Cellular neural networks for image analysis using steep slope devices. International Conference on Computer Aided Design. 92–95. 3 indexed citations

12.

Sedighi, Behnam, et al.. (2014). Impact of steep-slope transistors on non-von neumann architectures: CNN case study. Design, Automation, and Test in Europe. 137. 5 indexed citations

13.

Hu, Xiaobo Sharon, et al.. (2014). Design of 3D nanomagnetic logic circuits: a full-adder case study. Design, Automation, and Test in Europe. 119. 2 indexed citations

14.

Hu, Xiaobo Sharon, et al.. (2013). TFET-based cellular neural network architectures. 236–241. 18 indexed citations

15.

Niemier, Michael & S. Kurtz. (2013). Nanomagnet logic: architectures, design, and benchmarking. PhDT. 2 indexed citations

16.

Hu, Xiaobo Sharon, et al.. (2013). Systematic design of nanomagnet logic circuits. Design, Automation, and Test in Europe. 1795–1800. 1 indexed citations

17.

Porod, Wolfgang, Edit Varga, György Csaba, et al.. (2012). NanoMagnet logic. 4 indexed citations

18.

Niemier, Michael, Gary H. Bernstein, György Csaba, et al.. (2011). Nanomagnet logic: progress toward system-level integration. Journal of Physics Condensed Matter. 23(49). 493202–493202. 129 indexed citations

19.

Hu, Xiaobo Sharon, et al.. (2010). Design and comparison of NML systolic architectures. 29–34. 13 indexed citations

20.

Murphy, Richard C., et al.. (2001). Petaflop Computing for Protein Folding.. PPSC. 9 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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