Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine...

Area of Science:

• Materials Science
• Nanotechnology
• Electrical Engineering

Background:

• Two-dimensional layered materials (2DLMs), inspired by graphene, show promise but face challenges in high-quality growth and industrial integration.
• Existing fabrication methods for 2DLMs like molybdenum disulfide (MoS2) do not meet industrial standards for electronic devices.

Purpose of the Study:

• To overcome challenges in 2DLM fabrication and integration using machine learning (ML).
• To co-optimize the electrical characteristics of MoS2 top-gated field-effect transistors (FETs) for improved device performance.

Main Methods:

• Utilized ML algorithms to identify critical process parameters affecting MoS2 FET electrical properties.
• Employed ML combined with grid searching for wafer-scale fabrication optimization.
• Developed 62-level SPICE models for MoS2 FETs to simulate circuit behavior.

Main Results:

• Achieved co-optimization of key device parameters including mobility, threshold voltage, and subthreshold swing.
• Successfully constructed functional digital, analog, and photodetection circuits using ML-optimized MoS2 FETs.
• Demonstrated wafer-scale test FET arrays and a 4-bit full adder using industry-standard processes.

Conclusions:

• ML-assisted fabrication optimization is a viable strategy for advancing beyond-silicon electronic materials.
• The study validates the potential of ML for high-quality, wafer-scale production of 2DLM-based electronic devices.
• Results pave the way for the industrial application of MoS2 and other 2DLMs in next-generation electronics.