The state‐of‐the‐art conception of a bionic/robotic eye is a somewhat bulky multipart system comprising a video camera connected to a processing unit that in turn communicates data through a wireless transmitter to either an in vivo retinal implant or a computer system. An artificial cogni‐retina is a millimeter‐scale, intelligent apparatus designed as a replacement for these systems, while executing simple image processing tasks. As a bionic limb, it can connect directly to the optic nerve and perform rudimentary cognitive functions such as perceiving, learning, remembering, and classifying elementary visual data. This theoretical system presents a quantum leap in terms of size, power consumption, and speed to both prosthetic human eyes and robotic vision in artificial intelligence‐based platforms such as autonomous vehicles. Recently, an increasing number of publications have used interesting materials in artificial synaptic devices that drive this idea closer toward becoming a real‐world application. Such devices may form a basis for hardware‐based deep learning artificial neural networks that can potentially execute image processing tasks within a single clock cycle compared to software algorithms running on conventional von Neumann machines that require millions of cycles to perform image sensor interfacing, memory fetch operations, and data path propagation.

This study investigates the underlying mechanisms of multiple conductive filaments (CF) creation in metal-ion based conductive bridge RRAM (CBRAM) by using the Metropolis Monte Carlo algorithm and suggests a possible explanation for this phenomenon. The simulation method is demonstrated over a Cu/HfO2 structure, starting from a random initial distribution of oxygen vacancies (OV) defects in the resistive switching layer, to a formed CF and ending in a ruptured state. the results indicate that “Hot Spots” (HS), where agglomeration of OV trap like states for electron hopping based conduction induce local heating, create favorable energy conditions to attract diffused metal species originating from the top electrode. While HS may be created and annihilated by random OV generation and recombination processes, the precipitated metal forms a stem out of which a CF could evolve. The CF stem’s final growth stage is mainly driven by drift and diffusion. This process may lead to the formation of one or more CFs as a function of the forming bias voltage. This bias dependence is demonstrated over a large range, where the creation of a single, double and multiple CFs are shown. In addition, the reset process of the multi CF device is presented, and the experimentally observed, step like, gradual CBRAM reset is verified. The simulated results are in good agreement with experimental data and promote the idea that OV defect engineering may be used to improve CBRAM performance.

Integrated circuits designed to perform mathematical operations, such as Fourier transforms and matrix multiplications, in artificial visual perception and intelligent image processing are mainly constructed of conventional logic gates. However, Boolean logic is probably not the most optimal approach for brain‐inspired computing due to the fuzzy nature of biologic neural networks. This work demonstrates an application based on programmable fuzzy‐logic gates capable of combined photoelectric computations. Such an apparatus may be used to perform image compression immediately upon acquisition without having the need to rely on interaction between separate processor and sensor modules. It is based on resistive memory devices capable of state transitions in response to both electronic and light stimulations. Material nonimplication and logical true operations are first presented. A more complex functionality for material nonimplication of a logic conjunction is then demonstrated. These gates are then used as building blocks in the design and simulation of a configurable matrix multiplication unit that effectively implements in situ image compression. A membership function (FUZZIFY) that may be used to map strict logic levels to incremental fuzzy analog ones is also shown. Finally, an approach for integrating conventional logic with a fuzzy computation is discussed.