Login / Signup

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Min-Kyu SongJi-Hoon KangXinyuan ZhangWonjae JiAlon AscoliIoannis MessarisAhmet Samil DemirkolBowei DongSamarth AggarwalWeier WanSeok-Man HongSuma George CardwellIrem BoybatJae-Sun SeoJang-Sik LeeMario LanzaHanwool YeonMurat OnenJu LiBilge YildizJesús A Del AlamoSeyoung KimShinhyun ChoiGianluca MilanoCarlo RicciardiLambert AlffYang ChaiZhongrui WangHarish BhaskaranMark C HersamDmitri StrukovH-S Philip WongIlia ValovBin GaoHuaqiang WuRonald TetzlaffAbu SebastianWei LuLeon ChuaJ Joshua YangJeehwan Kim
Published in: ACS nano (2023)
Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.
Keyphrases
  • artificial intelligence
  • machine learning
  • big data
  • current status
  • deep learning
  • working memory
  • oxide nanoparticles