Computing-in-Memory with Thin-Film-Transistors: Challenges and Opportunities

Abstract

Abstract Thin-film transistor (TFT) has attracted enormous interests recently for its great potential in a wide range of edge computing applications, benefitting from its large-area low-cost flexible fabrications, and well integration with sensors and displays. With the support of in-situ processing of sensor data, TFT-based edge systems show their advantages in large-scale dense sensing with real-time energy-efficient processing and interaction, and more excitingly, it provides the opportunity of eliminating the massive data transfer to the cloud servers. However, the design of high-performance processing modules based on TFT is difficult, due to large device variation, poor stability, and low mobility. Computing-in-memory (CiM), which has been proposed recently as a high-efficiency high-parallelism computing approach, is expected to improve the capacity of TFT-based edge computing systems. In this survey, various recent works on TFT-based CiM have been summarized, showing the superiority to conventional processing flow by efficient in-memory analog computation with mitigation of data transfer, and reduced ADC usage for sensor data. With both opportunities and challenges, the design space and trend of TFT-based CiM to be explored are then described. Finally, further development and co-optimization from device to system are discussed for flourishing of the next-generation intelligent TFT-based edge system.