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

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.