Materials 2021, 14, 5223. https://doi.org/10.3390/ma14185223 www.mdpi.com/journal/materials Article In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor Nikolaos Vasileiadis 1,2, *, Vasileios Ntinas 2 , Georgios Ch. Sirakoulis 2 and Panagiotis Dimitrakis 1, * 1 Institute of Nanoscience and Nanotechnology, National Center of Scientific Research “Demokritos”, 15341 Agia Paraskevi, Greece 2 Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTh), 67100 Xanthi, Greece; vntinas@ee.duth.gr (V.N.); gsirak@ee.duth.gr (G.C.S.) * Correspondence: n.vasiliadis@inn.demokritos.gr (N.V.); p.dimitrakis@inn.demokritos.gr (P.D.); Tel.: +30-210-650-3118 Abstract: State-of-the-art IoT technologies request novel design solutions in edge computing, result- ing in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promis- ing technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demon- strated a CMOS compatible silicon nitride (SiNx) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dy- namics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspec- tive was discussed. Keywords: resistive random-access memory (RRAM); resistance switching; silicon nitride; memristor; vision sensor; photodiode; crossbar; in-memory computing; edge computing; dot product engine; IoT; SPICE 1. Introduction During the last decade, it became apparent that created data are increasing rapidly, requesting revolutionary solutions when memory and storage is concerned. These needs are more demanding in case of Internet of Things (IoT) applications and the correspond- ing IoT sensors that produce zettabytes of data nowadays. The most straightforward ap- proach to tackle the uprising urgent issue is the local pre-processing of the unstructured data generated by the IoT sensors in an edge-based sense [1–4]. Such a promising solution will eventually minimize the requesting power consumption of the corresponding IoT applications and at the same time advance the overall computing in terms of energy effi- ciency. However, following conventional digital design approaches involving either spe- cialized signal processors or more generic microcontrollers does not prove as promising as expected and especially when power consumption is highly demanded [5]. The next obvious step of utilizing a more specific-oriented CMOS-based design can be significantly enhanced with the addition of novel nanoelectronic devices with memory abilities, namely memristors, to be combined with the ΙοΤ sensors. To further investigate the prom- ising aspects of such a hardware approach enabling also in-memory computation at IoT sensors, special interest is on vision sensors as a fine candidate for edge computing. The vision sensors, when integrated with such processing hardware, are enabled to provide Citation: Vasileiadis, N.; Ntinas, V.; Sirakoulis, G.C.; Dimitrakis, P. In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor. Materials 2021, 14, 5223. https://doi.org/ 10.3390/ma14185223 Academic Editor: Stephan Menzel and Katarzyna Bejtka Received: 28 June 2021 Accepted: 7 September 2021 Published: 10 September 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (http://crea- tivecommons.org/licenses/by/4.0/).