Topological Insulator based Transistors for Neuromorphic Computer Systems
Lead Research Organisation:
University of Sheffield
Department Name: Electronic and Electrical Engineering
Abstract
As dimensional scaling of CMOS is approaching fundamental limits, new information processing devices and micro-architectures are required to extend the capability of present-day Von-Neumann based computers. Application pulls for the new information age such as big data, Internet of Things (IoT), autonomous systems, exascale computing require artificial intelligence (AI) chips that can operate on super ultra-low power to ultimately match and exceed the efficiency of the human brain.
A leading candidate for such low power computing architecture is the spiking neural network (SNN), which is one type of event-based learning without any external memory that consumes very little energy. However, the hardware component that emulates the decision-making process of a SNN - a neuron, consumes high power and therefore presently limits the performance of AI.
This project brings together expertise from University of Glasgow and Sheffield to explore ternary Topological Insulator (Bi2Te2Se) as a low voltage material platform for a leaky integrate and fire neuron. By controlling the device surface states, the conductance of the channel will be tuned, sufficient enough to transmit a current spike from the drain to the source terminals. A successful demonstration of this concept will result in a milestone leap in hardware implementations of Artificial Neural Networks. The work is exciting and adventurous because there are only two reported TTI-FETs in the literature so far (from 2011and 2012 respectively), and neither of them is being envisaged to be operated as we plan in this work. Beyond the stated goals, new knowledge will be generated of interest to materials science, spintronics and quantum computing communities, where the knowledge gained from this project offers the potential to address existing bottlenecks in these research fields.
A leading candidate for such low power computing architecture is the spiking neural network (SNN), which is one type of event-based learning without any external memory that consumes very little energy. However, the hardware component that emulates the decision-making process of a SNN - a neuron, consumes high power and therefore presently limits the performance of AI.
This project brings together expertise from University of Glasgow and Sheffield to explore ternary Topological Insulator (Bi2Te2Se) as a low voltage material platform for a leaky integrate and fire neuron. By controlling the device surface states, the conductance of the channel will be tuned, sufficient enough to transmit a current spike from the drain to the source terminals. A successful demonstration of this concept will result in a milestone leap in hardware implementations of Artificial Neural Networks. The work is exciting and adventurous because there are only two reported TTI-FETs in the literature so far (from 2011and 2012 respectively), and neither of them is being envisaged to be operated as we plan in this work. Beyond the stated goals, new knowledge will be generated of interest to materials science, spintronics and quantum computing communities, where the knowledge gained from this project offers the potential to address existing bottlenecks in these research fields.
Organisations
Publications
Assi D
(2023)
Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality
in Advanced Electronic Materials
Assi D
(2023)
Charge-Mediated Copper-Iodide-Based Artificial Synaptic Device with Ultrahigh Neuromorphic Efficacy
in physica status solidi (RRL) - Rapid Research Letters
Assi DS
(2023)
Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems.
in Advanced science (Weinheim, Baden-Wurttemberg, Germany)
| Description | We have designed and demonstrated a quantum topological insulator (QTI) Bi2Se2Te based synaptic neuroelectronic device that can address age-related degradation of neuronal signals. The device was based on an amorphous material achieved by electrodeposition technique. We have attempted to use a drop-cast technique with some flakes purchased from Ossila (@Sheffield) on bottom-gated templates supplied by them, however, without success so far. In parallel, we have established contact with two US universities (Rutgers and Penn state) to supply us with MBE grown high quality material, with the right level of resistivity, which we hope will help us to resolve the issue before the end date of this grant. |
| Exploitation Route | Quantum edge state switching properties of electrochemically deposited Bi2Se2Te topological insulator materials were used to evaluate neuromodulation to address age distorted signals in elderly individuals. This is a low power technology that could potentially be useful in brain-computer interfacing. |
| Sectors | Electronics Healthcare |