The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive switching materials (RSMs) based on different physical principles have emerged for memories that could enable energy-efficient and area-efficient in-memory computing. In this Review, we survey the four physical mechanisms that lead to such resistive switching: redox reactions, phase transitions, spin-polarized tunnelling and ferroelectric polarization. We discuss how these mechanisms equip RSMs with desirable properties for representation capability, switching speed and energy, reliability and device density. These properties are the key enablers of processing-in-memory platforms, with applications ranging from neuromorphic computing and general-purpose memcomputing to cybersecurity. Finally, we examine the device requirements for such systems based on RSMs and provide suggestions to address challenges in materials engineering, device optimization, system integration and algorithm design.

大数据时代信息的快速增长要求改变信息处理范式，这反过来又需要新的电路构建块，以克服晶体管定标成本效益下降和在非晶体管中使用晶体管的固有效率低下的问题。 -von Neumann计算架构。因此，已经出现了基于不同物理原理的电阻开关材料（RSM）用于可以实现节能和面积高效的内存中计算的存储器。在这篇评论中，我们调查了导致这种电阻切换的四种物理机制：氧化还原反应，相变，自旋极化隧穿和铁电极化。我们将讨论这些机制如何为RSM配备理想的属性，以用于表示能力，切换速度和能量，可靠性和设备密度。这些特性是内存处理平台的关键推动力，其应用范围从神经形态计算和通用内存计算到网络安全。最后，我们检查了基于RSM的此类系统的设备要求，并提出了解决材料工程，设备优化，系统集成和算法设计方面的挑战的建议。