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Related Experiment Video

Updated: Apr 9, 2026

Fabricating van der Waals Heterostructures with Precise Rotational Alignment

Fabricating van der Waals Heterostructures with Precise Rotational Alignment

Published on: July 5, 2019

Heterogeneous 3D Integration Based on Atomic-Level Electronics.

Hyunho Seok^1,2,3, Sihoon Son^2,3, Hyunbin Choi⁴

¹Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

ACS Applied Materials & Interfaces

|April 7, 2026

Summary

This summary is machine-generated.

We developed a novel monolithic 3D integration (M3D) platform for neuromorphic computing, overcoming bandwidth limits with vertically integrated atomic-scale electronics and memristive devices for efficient AI processing.

Keywords:

2d materials convolutional neural network heterogeneous integration memristor monolithic 3d integration neuromorphic computing

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Last Updated: Apr 9, 2026

Fabricating van der Waals Heterostructures with Precise Rotational Alignment

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Residue-Free Fabrication of van der Waals Heterostructures of Two-Dimensional Materials

Residue-Free Fabrication of van der Waals Heterostructures of Two-Dimensional Materials

Published on: July 18, 2025

Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

Multimodal and edge AI systems face challenges with bandwidth, data movement, and thermal limitations in conventional 3D integration.
Existing architectures struggle to efficiently process complex data streams at the edge.

Purpose of the Study:

To introduce a monolithic 3D integration (M3D) of a heterogeneous neuromorphic platform.
To overcome the limitations of conventional 3D integration for AI systems.

Main Methods:

Vertically integrating atomic-scale electronics with complementary memristive devices.
Utilizing ultralow-power van der Waals transistors, WS2 conductive-filament memristors, and Ag-MoS2 diffusive memristors.
Forming a compact neuromorphic stack for synaptic plasticity, temporal learning, and in-memory computation.

Main Results:

Demonstrated analog vector-matrix multiplication for CNN inference and image filtering with 93.1% CIFAR-10 accuracy.
Achieved energy-efficient, beyond-von Neumann computation.
Enabled heterogeneous functional partitioning and programmability across vertically integrated tiers.

Conclusions:

The proposed 2D-material-based platform offers a unified 3D architecture for multimodal neuromorphic operation.
Layer-level specialization and intertier signal coupling support learning and system-level functionality.
This M3D approach overcomes bandwidth bottlenecks and data-movement overhead in edge AI.