In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor

• Main Authors: Nikolaos Vasileiadis, Vasileios Ntinas, Georgios Ch. Sirakoulis, Panagiotis Dimitrakis
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Materials
• Subjects: resistive random-access memory (RRAM); resistance switching; silicon nitride; memristor; vision sensor; photodiode
• Online Access: https://www.mdpi.com/1996-1944/14/18/5223

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting 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 promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiN_x) 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 dynamics 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 perspective was discussed.