FeFET Local Multiply and Global Accumulate Voltage-Sensing Computation-In-Memory Circuit Design for Neuromorphic Computing

This work presents a design of voltage-sensing Computation-in-Memory (CiM) using ferroelectric FET (FeFET) in the point of device and circuit for neuromorphic computing. FeFET CiM with Local Multiply and Global Accumulate (LM-GA) operation works for multiply-accumulate (MAC) of artificial neural networks (ANNs) and integrate operation of spiking neural networks (SNNs). The high scalability and high on/off ratio of FeFETs contribute to large-capacity CiM for neural networks. For the device design, measured FeFET characteristics by source-follower read show small variation in read-disturb and data-retention. The circuit design of FeFET LM-GA CiM is discussed with evaluations of wiring capacitance and resistance, threshold voltage shift, and gate timing. In addition, integration operation of SNNs, which is not possible with current-sensing CiM, is possible with FeFET LM-GA CiM.