A one‐selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high‐density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10−7 A), a high ON/OFF current ratio (106), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA‐based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein‐based 1S1R array to achieve high‐density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.)

The logic gate is the basic unit of a digital circuit structure. The operation, memory, I/O, and other reading and writing functions of computer systems require logic circuits. Logic gates based on resistive memory can make existing integrated circuits denser, smaller, faster, and use fewer devices. In this paper, Al/polymethyl methacrylate (PMMA)/egg albumen (EA):Au nanoparticles/PMMA/Al multilayer biological resistive random access memory was prepared based on the natural biological material—egg albumen (EA). The device has bipolar switching behavior, a higher switching current ratio, a lower threshold voltage, and better stability. A circuit based on auxiliary logic is constructed using this device, and the logic functions of AND, OR, NOT, NAND, and NOR are realized. This device provides an effective potential solution for implementing high-performance electronic devices and large-scale integrated circuits.

As a promising solution for overcoming the bottlenecks of traditional von Neumann computers, the hardware implementation of neuromorphic computing has attracted increasing interest. High-performance artificial synapses are the basic units of brain-like chips and are important for achieving efficient neuromorphic calculations. This paper reports the fabrication of Al/chitosan (CS)/graphene oxide (GO)/indium tin oxide (ITO) artificial synapses. The electronic insulation and proton conduction properties of CS enable it to conduct electricity alone and accelerate the movement of oxygen vacancies in GO. In particular, an experimental device successfully simulated the Pavlov associative memory experiment and was made to exhibit both short-term and long-term memory capabilities by modifying the external stimuli. This device provides a possible avenue to realize neuromorphic engineering on the basis of biomemristors.

Resistive random access memory (RRAM) is integrated computing and storage, but most devices have problems, such as high power consumption and poor consistency, so it is critical to optimize the performance of the device. In this paper, amylum RRAM is prepared by using the quantization effect of gold nanoparticles (Au NPs), which not only improves the consistency of the threshold voltage distribution of the device but also reduces the power consumption of the device and significantly increases the ON/OFF current ratio of the device. This work not only realized multilevel data storage (4 levels, 2 bits) on the same unit, improving the storage density of starch RRAM, but also simulated the synaptic potentiation and depression behavior, providing a way of thinking for the next generation of artificial intelligence and brain-like neural computing.

Most current resistive memory has the problems of high and unstable threshold voltages and high device misread rates caused by low current switching ratios. To address these problems, an Al/poly(methyl methacrylate) (PMMA)/silkworm hemolymph:gold nanoparticles/PMMA/indium tin oxide memory device is fabricated by adding PMMA layers above and below the active layer. The device not only has stable bipolar switching characteristics with a high ON/OFF current ratio but also has a lower and more stable threshold voltage. Potentiation, depression, and spike‐time‐dependent plasticity at biological synapses are realized using this device. The device is successfully fabricated on a flexible substrate, and the device can still maintain a stable working state after 104 bending cycles. This research opens a new door for the future realization of artificial synapses in neural network hardware.