Hadke Shreyash, Kang Min-A, Sangwan Vinod K, Hersam Mark C
Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.
Department of Chemistry, Northwestern University, Evanston, Illinois 60208, United States.
Chem Rev. 2025 Jan 22;125(2):835-932. doi: 10.1021/acs.chemrev.4c00631. Epub 2025 Jan 2.
Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions.
受大脑启发的计算领域的最新突破有望解决从安全到医疗保健等广泛问题。然而，目前使用传统硅硬件实现人工智能算法的策略正导致不可持续的能源消耗。基于模仿生物系统的电子设备的神经形态硬件正在成为一种低能耗的替代方案，尽管要取得进一步进展需要能够在保持可扩展性和速度的同时模仿生物功能的材料。由于其多样的独特性质，原子级薄的二维（2D）材料有望成为下一代电子产品的构建模块，包括非易失性存储器、内存和神经形态计算以及灵活的边缘计算系统。此外，二维材料实现了超越传统逻辑和存储系统的逼真的突触和神经元反应。在这里，我们全面综述了用于神经形态电子和光电器件、电路及系统的二维材料和范德华异质结的生长、制造和集成。对于每种情况，都强调了物理性质与器件响应之间的关系，随后对不同应用的技术进行了批判性比较。我们以对利用二维材料和异质结的基本特性的神经形态应用中仍然存在的关键挑战和机遇的前瞻性观点作为总结。
