TFT-Based Near-Sensor In-Memory Computing: Circuits and Architecture Perspectives of Large-Area eDRAM and ROM CiM Chips

In the era of intelligent IoT, huge amount of sensor data is collected and then transmitted to processor elements in edge devices or cloud servers. The latency and energy consumption in this process have been a bottleneck and are becoming more severe. To mitigate this problem, the idea of combining sensors, memory and processors for collectively handling the data, has been proposed and explored actively in recent efforts. In this work, thin-film transistor (TFT), which has been widely adopted in display devices and flexible sensors, is exploited. It is shown that, while TFT is promising for large-area sensing, it also shows a great potential for computing and storing data for large-area and low-cost edge sensors. More specifically, we have fabricated and measured two large-area TFT-based near-sensor computing-in-memory (CiM) chips adopting embedded DRAM (eDRAM) and ROM structure respectively. We further give a detailed analysis of the integration of CiM arrays and sensor arrays to realize a sensing and data pre-process system. Measurement and simulation results show that such TFT-based solutions can accomplish real-time sensing and multiply-accumulate (MAC) processing in the analog field, which simplifies the system design with lowered energy and latency in our neural network evaluations.