Memristors are a category of circuit devices representing the relationship between magnetic flux and charge, which is one of the hot spots in international scientific research. In the realm of biomedicine, memristors have important application prospects in biosensing, neurobionics, brain-computer interface, drug delivery and regenerative medicine, etc. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic chips due to their innate ability to emulate the interconnection and information transfer processes among neurons.

To meet these major application requirements, the teams of Sun Bai and Ren Fenggang from National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine (Surgical Dreamworks) of the First Affiliated Hospital (FAH) of Xi'an Jiaotong University (XJTU) systematically reviewed the research progress in memristors-based neuromorphic chips and published a review article entitled "Memristor-based Neuromorphic Chips" in Advanced Materials with an IF of 29.4 in 2022 (JCR Q1), one of the most influential top journals in this field. Duan Xuegang, a graduate student from XJTU, is the first author. Distinguished Professor Sun Bai from Surgical Dreamworks is the corresponding author. Ren Fenggang, Associate Researcher from Surgical Dreamworks and Department of Hepatobiliary Surgery, and Professor Zhou Guangdong from Southwest University and Professor Wu Zhenhua from Shanghai Jiao Tong University are co-authors. The FAH of XJTU is the first affiliation of this article.

This review article introduces the design principle of memristors-based neuromorphic chips, and describes the working principle, characteristic features and applications of memristors in the field of neuromorphic chips. The memristor array, as the key component of neuromorphic chips, is thoroughly discussed, and the present mainstream neural networks are delved. The design of neuromorphic chips is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. The key performance metrics of memristors-based neuromorphic chips as well as the key metrics related to memristor devices are employed to realize both the synaptic and neuronal components are also highlighted. The findings in this article provide significant guidance for the development and transformation application of memristors-based neuromorphic chips.