Ferroelectric Field Effect Transistor (FeFET) memory has shown the potential to meet the requirements of the growing need for fast, dense, low power and non-volatile memories.Integrating a layer of ferroelectric within the gate stack of a regular Field Effect Transistor (FET) enables the transistor to store data in the polarization state of the ferroelectric.
In this project, we look for appropriate application of binary neural network (BNN) which can benefit from the logic implementation within the memory.
Background:
Ferroelectric Field Effect Transistor (FeFET) memory has shown the potential to meet the requirements of the growing need for fast, dense, low power and non-volatile memories.Integrating a layer of ferroelectric within the gate stack of a regular Field Effect Transistor (FET) enables the transistor to store data in the polarization state of the ferroelectric. 1T-FeFET memory arrays considers as promising technologies and are intensively explored. Since the FeFET is firstly a transistor, usage for logic implementation are explored too, mainly for accelerators implementations of different neural networks.
Project:
This project is a continue to project that showed the ability of implementation of OR, AND, XOR, XNOR and CAM functions within a ferroelectric memory array. In this project, we look for appropriate application of binary neural network (BNN) which can benefit from the logic implementation within the memory. The target is to show an implementation of this application and compare it over state-of-the-art technologies. This project is part of research and is suitable for undergraduate students who are thinking about higher degree.
The comparison will be handed using Spice model of FeFET using Cadence Virtuoso to conduct the simulations.
Relevant courses: Introduction to VLSI, Electronic circuits
For more information: Mor Dahan – mordahan@campus.technion.ac.il