## 1 Introduction  
The development of the Internet of Things and the era of big data is a fundamental problem in the field of Internet information technology. The Internet of Things and the era of big data, the ability of machine data processing to reach the level of human brain cognition and learning is an important goal in the field of Internet information technology, including cloud computing, data mining, machine learning, and artificial intelligence (AI). The technological change has brought about the exponential generation of data, and the \"memory wall\" caused by frequent data movement between memory and processor has aggravated the \"von Neumann bottleneck\". With the end of Moore's Law, it becomes difficult to improve the performance of chips and systems through chip minimization. Therefore, it is urgent to innovate a computing paradigm to solve the power-consumption and computing-power problems induced by the rapid development of data-centric applications. Meanwhile, the demand for energy-sensitive electronic devices with superior computing power is ubiquitous in the era of the Internet of Things, especially for edge applications. Devices are the basis of the computing network of the Internet of Things. Millions of devices not only support the communication between machines but also constantly generate data to exchange information by controlling the complex network of intelligent infrastructure. With the improvement in the computing power of devices, the scale of the network grows larger and larger, and the learning ability grows stronger and stronger. However, the ability to integrate low-cost and high-density devices and the array size are also crucial for evaluating the feasibility of chip integration. The neuromorphic system simulates the storage, computing, and information expression architecture of the biological brain, which processes massive amounts of data with extremely low power consumption and high speed. It is the core technology supporting the development of emerging applications such as artificial intelligence. [PERSON] et al. proposed to use the 28 nm process to manufacture the neuromorphic computing chip \"Tianjic\". A single chip supports 40,000 neurons and 10 million synapses, which can simultaneously support computer-science-oriented and neuroscience-oriented computing modes, but it is still difficult to achieve the scale and power consumption of the human brain [1]. The rapid development of information processing depends on the progress of underlying electronic devices. In the research and development of neuromorphic devices, many materials show resistive-switching characteristics under electric fields or current stimulation. The performance of memristors based on traditional bulk materials is highly dependent on atomic scale defect structures and has limited adjustability to neuromorphic functions. However, they have the advantages of good CMOS compatibility, multilevel states, and simple chemical composition, such as HfO\({}_{2}\), ZnO, NiO, TiO\({}_{2}\), WO\({}_{3}\), and TaOx [2]. Due to the advantages of dangling bond-free lattices and easily available single-layer (less than 1 nm in thickness) structures, emerging two-dimensional materials (such as WSe2, h-BN, BP, and MoS2) have become popular research topics in the field of hyper-scaling electronic devices, such as matrix computing performance enhancement, continuous scaling of transistor size, logic gates, and highly integrated chips, to achieve more efficient memory and computing integration. [PERSON] et al. integrated a single-layer MoS\({}_{2}\) transistor with a metal oxide resistor random access memory (RRAM), created two transistor double resistor (2T2R) transition metal hydride TCAM (TMD-TCAM) cells, made full use of the high-performance and low leakage performance of two-dimensional materials, obtained a high resistance ratio, realized the parallel search for a large number of data bits, and was compatible with the back-end (BEOL) metal interconnection process [3].