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Semiconductors01:22

Semiconductors

There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...

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Wafer scale WS2based ultrafast photosensing and memory computing devices for neuromorphic computing.

Sharmila B1, Priyanka Dwivedi1

  • 1Indian Institute of Information Technology (IIIT) Sri City, Chittoor 517646, India.

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|July 8, 2024
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Summary

This study introduces integrated sensing and computing memory (ISCM) devices using tungsten disulfide (WS2) for Internet of Things (IoT) applications. These devices achieve high performance in photosensing, memory functions, and image classification, enabling smart sensor development.

Keywords:
2D materialsISCM devicesWS2bipolar resistive switchingmemristorsphotosensing

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computer Engineering

Background:

  • The Internet of Things (IoT) requires integrated devices for sensing and data processing.
  • Memristors offer potential for combining memory and computing functionalities.
  • Optical sensors are crucial for data acquisition in various IoT applications.

Purpose of the Study:

  • To develop and characterize integrated sensing and computing memory (ISCM) devices using tungsten disulfide (WS2).
  • To evaluate the performance of these ISCM devices for neuromorphic computing and image classification.
  • To demonstrate the scalability and stability of the fabricated devices for real-time IoT applications.

Main Methods:

  • Fabrication of ISCM devices using microfabrication techniques on a 2-inch wafer.
  • Characterization of photosensing and memory properties using electrical and optical stimulations.
  • Evaluation of device performance for image classification through potentiation and depression tests.

Main Results:

  • The fabricated photosensing device exhibits high responsivity (8 A W-1), detectivity (2.85 × 1011 Jones), and fast response times (80.2/78.3 μs).
  • The memory component shows rapid set/reset times (51.6/73.5 μs).
  • Image classification accuracy of 98.27% was achieved using the device's potentiation and depression characteristics.

Conclusions:

  • The developed WS2-based ISCM devices are stable, reproducible, and suitable for real-time IoT applications.
  • The integration of sensing and memory functionalities in a single device paves the way for advanced neuromorphic computing.
  • These findings support the fabrication of smart sensors with enhanced, integrated capabilities.