Computing-in-memory with thin-filmtransistors: challenges and opportunities

Abstract

Thin-film transistors (TFTs) have attracted significant interest recently fortheir great potential in a wide range of edge computing applications, due to their advantages such as 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, they provide the opportunity to eliminate 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 thispaper, 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 analog-to-digital converter 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 the flourishing of the next-generation intelligent TFT-based edge system.