# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 2 State-of-the-Art Studies
### Memristor-Based Systems for Machine Vision
), for the most optimal exploitation of computational resources all data processing in such systems can be performed completely in analog form, as was proposed in [8]. In the photosensors of computer vision systems, information is captured in analog form, and it is reasonable to connect them to the memristor-based ANNs without ADCs and DACs [37], implementing the concept of \"in-sensor computing\" [38]. As a result, there is no need to use software algorithms and models interacting with computer memory to load model parameters and store intermediate results. The system requires significantly fewer electronic components, becoming potentially faster and more energy efficient. In general, any electronic device capable of generating current (or changing the current strength in a circuit) depending on the intensity of illumination can be used as a photosensor in such a system (Figure 1). Figure 1: The main features of the neuromorphic analog machine vision systems, combining “in-memory computing”, “in-sensor-computing”, and neuromorphic architectures. **(A)** In comparison with the existing digital computer vision systems, “in-memory computing” makes it possible to process visual information entirely within the hardware when the ANN models work completely in analog form using computers based on memristive devices. **(B)** In comparison with the existing general memristor-based computers, in the neuromorphic analog machine vision systems, there are no analog-to-digital and digital-to-analog conversions in the process of capturing visual information via photosensors. For these purposes, the devices for “in-sensor computation” can be used in the sensory part of a system for capturing visual information in analog form, which is then fed to an ANN based on memristors. In this paper, in order to illustrate the proposed architecture, we have considered the use of photodiodes connected with memristive devices for making the sensory part; however, there are alternative ways to achieve this. Over the past several years, great progress has been provided in photo-sensing memristors based on SiO\({}_{\text{x}}\) RRAM devices [39], MoS\({}_{2}\) photosensitive field effect transistors [40], 2D materials-based photo-memristors [41], thin carbyne-gold films [42], etc. Comprehensive analyses of state-of-the-art memristor-based sensors for edge detection, mean filtering, stylization, and recognition have been recently published in [43]. The coupling of memristors with photosensors shows that this approach can simulate some retinal functions [44; 45]. Some ways in which one can integrate the array of photosensors with the array of memristors have been proposed in [43; 46]. In particular, the authors of [43] proposed a 1D1M vision sensor's schematic layout for CMOS-compatible silicon nitride (SiN\({}_{\text{x}}\)) memristive devices and demonstrated its use for image capturing and mean filtering. The authors of [46] proposed a reconfigurable three-dimensional hetero-integrated technology for vertically stacking a diverse range of functional layers such as sensors, processors, and memory of a single-material system (for example, silicon). Such decisions could provide energy-efficient sensor computing systems for edge computing applications.