Low‐Power Self‐Rectifying Memristive Artificial Neural Network for Near Internet‐of‐Things Sensor Computing
Seok Cheol Choi, Yong Yook Kim, Tien Van Nguyen, Won Hee Jeong, Kyeong‐Sik Min, Byung Joon Choi
Abstract
Abstract Frequent data transfers between Internet‐of‐Things (IoT) sensors and cloud servers consume energy and lead to latency—a bottleneck for ubiquitous computing. To reduce the need for such enormous data transfers, the combined function of IoT sensors and near‐sensor artificial neural networks can process data properly before they are transferred to cloud servers. Herein, energy‐efficient memristor crossbar arrays are demonstrated for image recognition tasks that are potentially adopted for IoT sensors. The adoption of the selector‐free memristor device with a self‐rectifying function allows for simple stacking of metal–dielectric–metal layer, thus significantly simplifying the fabrication process while achieving low‐current operation (<10 µA in microdevice). Area‐dependent resistive switching characteristics and the incorporation of interface effects reveal the role of the switching and rectifying phenomena in such devices. Finally, the Modified National Institute of Standards and Technology pattern recognition task is demonstrated with 32 × 32 memristor crossbar arrays combining a SPICE simulation. Therefore, it is expected that self‐rectifying memristor arrays can pave the way for the development of more intelligent IoT sensors.