Energy-Efficient Logic-in-Memory and Neuromorphic Computing in Raised Source and Drain MOSFETs

This work highlights the potential application of raised source and drain (RSD) MOSFETs-based charge trapping memory (CTM) for next-generation computing applications. This simulation study presents a double gate (DG)-RSD MOSFET technology with a short gate length (50 nm) to significantly improve the performance of logic-in-memory (LIM) and neuromorphic computing (NC) systems. By taking advantage of the superior electrostatic control and reduced parasitic resistance provided by RSD MOSFETs, this work aims to reduce energy consumption for LIM and NC applications. The CTM operation is based on the FN tunneling mechanism, performing 16 Boolean logic functions in two steps: program and read operations, including OR, AND, NOR, NAND, XOR, and XNOR. Furthermore, the device shows synapse capability by mimicking long-term potentiation (LTP) and depression (LTD) while achieving good linearity and symmetricity between the conductance values. The results reveal that energy consumption for LIM is ~ 21.4 fJ and ~68.9 fJ for the NC application. This simulation result also demonstrates a high level of accuracy of 88.19%, with less than a 2.04% difference compared to software-based neural networks (90.23%). These multifunctional capabilities of DG-RSD-based CTM highlight the potential application for next-generation computing.