More-than-Moore Approaches Implemented Using van der Waals Heterostructures.

Journal: ACS nano
Published Date: Aug 5, 2025

Abstract

Two-dimensional (2D) materials and van der Waals (vdW) heterostructures have emerged as key enablers in addressing the fundamental limitations of silicon-based technologies, driving advancements in next-generation electronic systems. Their high carrier mobility, tunable electronic characteristics, and absence of dangling bonds, combined with their compatibility with thin-film fabrication and wafer-scale integration, allow for the seamless integration of memory, logic, and sensing into compact, energy-efficient architectures. This review highlights the transformative role of 2D materials and vdW heterostructures in reshaping computing paradigms, focusing on emerging computing (in-memory, in-sensor, bioinspired, probabilistic, and quantum) and digital security (true random number generator (TRNG) and physical unclonable functions (PUFs)). By overcoming memory-wall challenges and enabling ultralow latency and parallel processing, these advancements provide tailored solutions for artificial intelligence, edge computing, and the Internet of Things. Furthermore, the physical properties of 2D materials─including scalability, high carrier mobility, spin-orbit coupling, and quantum fluctuations─expand possibilities across computing domains. These properties not only enhance emerging computing technologies but also strengthen entropy-based random number generation and variability-driven security mechanisms in digital security applications. To fully realize these advancements and the transition from fundamental research to large-scale implementation, continued progress in materials engineering and device fabrication is essential for achieving scalable, energy-efficient, and multifunctional computing systems.

Authors

  • Sangmin Lee
    Department of Electronic Engineering, Inha University, Incheon 22212, Korea.
  • Yeong Kwon Kim
    School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu, 41566, Republic of Korea.
  • Jongmin Noh
    SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea.
  • Byung Chul Jang
    School of Electrical Engineering , Graphene/2D Materials Research Center, KAIST , Daejeon 34141 , Korea.
  • Sungjoo Lee
    From the Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul 06351, South Korea (H.K., J.H.W., J.H.K., Y.K.C., M.J.C.); Medical AI Research Center, Samsung Medical Center, Seoul, South Korea (Kyungsu Kim, M.J.C.); Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea (Kyungsu Kim, Kyunga Kim, M.J.C.); and Department of Health Sciences and Technology (S.J.O.) and Department of Digital Health (S.L., Kyunga Kim), Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea.

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