Tellurene Memristors for Neuromorphic Computing System Technology

Introduction

Big-data management is currently placing a high demand on both the hardware performance level, e.g. access latency, storage capacity, cost performance, and on the cognitive level, e.g. data processing, architectures, and algorithms. The ever-growing pressure for big data creates urgent global challenges like energy consumption and memory efficiency.

Challenges in Big Data

New device architectures beyond the von Neumann paradigm are demanded, which are inspired by the biological synaptic operativity towards the so-called neuromorphic computational scheme. The memristor is the more viable device emulating the synaptic behavior.

Requirements for Memristor Circuits

Advanced materials are required to make memristor-based circuits energetically sustainable and outperforming. The integration of 2D semiconductors in memristors is a promising path to tackle these global challenges within neuromorphic computation.

Proposed Solution

In this scenario, we propose a single-element memristor cell design based on tellurene (2D tellurium) mimicking artificial synaptic behavior and thus enabling memristor applications.

Advantages of Tellurene

Tellurene offers solid advantages over other 2D players owing to:

• The structural and chemical simplicity
• The low thermal budget of its synthesis
• The versatility to adapt to rigid and flexible layouts

Goals of the Project

Our goals are:

1. The scalable production of a tellurene standard
2. Its integration in single memristor cells
3. The development of cross-bar arrays of memristors aiming at the fabrication of neuromorphic circuits

Production Process

The peculiar production process makes tellurene fit for delamination and transfer to any kind of surface, either rigid or flexible, flat or curved, and readily available for testing in edge-computing applications like environmental sensing signal elaboration.

Technology Transfer

The translational purpose is to further up technology transfer of the developed products (either materials and processes) by retrieving stakeholders among small- and medium enterprises addressing the AI computing market.