Employing Ruthenium Interconnects for High-Density Neuromorphic Computing Architectures

The Promise of Neuromorphic Computing

Neuromorphic computing, inspired by the architecture of the human brain, has emerged as a transformative paradigm for artificial intelligence (AI) and machine learning (ML). Unlike traditional von Neumann architectures, neuromorphic systems integrate memory and processing in a massively parallel, energy-efficient manner. However, scaling these systems to high-density configurations while maintaining energy efficiency remains a critical challenge.

The Role of Interconnects in Neuromorphic Systems

Interconnects—the conductive pathways that link neurons and synapses—play a pivotal role in neuromorphic architectures. Traditional copper (Cu) interconnects face limitations due to:

• Electromigration: High current densities lead to atomic displacement, degrading performance.
• Resistance Scaling: Increased resistivity at nanoscale dimensions due to surface scattering.
• RC Delay: Higher resistance-capacitance delays at smaller nodes.

Ruthenium: A Viable Alternative

Ruthenium (Ru), a transition metal in the platinum group, has garnered attention as a potential replacement for Cu in high-density interconnects. Key advantages include:

• Lower Resistivity: Ru exhibits lower resistivity at ultra-thin dimensions compared to Cu.
• Superior Electromigration Resistance: Ru interconnects demonstrate higher current-carrying capacity before failure.
• Compatibility with Advanced Nodes: Ru can be integrated into sub-5nm fabrication processes.

Material Properties of Ruthenium

The intrinsic properties of Ru make it an attractive candidate:

• Resistivity: ~7.1 µΩ·cm (bulk), with minimal increase at scaled dimensions.
• Melting Point: 2334°C, enhancing thermal stability.
• Electromigration Activation Energy: ~1.8 eV, significantly higher than Cu (~0.7 eV).

Integration Challenges and Solutions

Despite its advantages, integrating Ru into neuromorphic interconnects presents technical hurdles:

Deposition Techniques

Ru thin films can be deposited via:

• Atomic Layer Deposition (ALD): Enables conformal, nanoscale film growth.
• Physical Vapor Deposition (PVD): Suitable for thicker interconnect layers.

Adhesion and Barrier Layers

Unlike Cu, Ru does not require a diffusion barrier, simplifying stack complexity. However, adhesion to dielectrics (e.g., SiO₂) may require interfacial engineering.

Etching and Patterning

Ru’s chemical inertness complicates etching. Reactive ion etching (RIE) using oxygen-based plasmas is commonly employed.

Energy Efficiency Gains

Neuromorphic systems demand ultra-low power operation. Ru interconnects contribute to energy efficiency through:

• Reduced IR Drop: Lower resistance minimizes voltage losses.
• Lower Joule Heating: Decreased power dissipation at high current densities.

Comparative Analysis: Ru vs. Cu

A 2022 study by IMEC demonstrated:

• Power Savings: Up to 30% reduction in interconnect power for Ru at 3nm nodes.
• Performance: 15% lower latency due to reduced RC delays.

Scalability for High-Density Architectures

Neuromorphic systems aim for synapse densities exceeding 10⁸/mm². Ru interconnects enable this through:

• Nanoscale Dimensions: Line widths below 10nm without significant resistivity penalty.
• 3D Integration: Compatibility with monolithic 3D stacking.

Case Study: IBM’s TrueNorth

IBM’s TrueNorth chip, a pioneering neuromorphic architecture, utilized Cu interconnects. Simulations suggest that replacing Cu with Ru could reduce energy consumption by ~20% while maintaining synaptic density.

Future Directions

The adoption of Ru interconnects in neuromorphic computing is still in its infancy. Key research areas include:

• Hybrid Interconnect Schemes: Combining Ru with graphene or carbon nanotubes for further performance gains.
• Thermal Management: Mitigating localized heating in ultra-dense arrays.
• Manufacturing Cost Reduction: Scaling ALD processes for mass production.

Conclusion

The transition to ruthenium interconnects represents a critical step toward realizing scalable, energy-efficient neuromorphic computing systems. While challenges remain in deposition and patterning, the material’s inherent advantages position it as a frontrunner for next-generation brain-inspired architectures.