Overview
Our device fabrication research focuses on creating hardware that mimics the functions of biological neural networks. By developing neuromorphic devices using memristors, transistors, and advanced nanoscale fabrication techniques, we aim to create efficient computing systems that can process information and learn in ways similar to the human brain, revolutionizing artificial intelligence applications.
Neuromorphic Devices
- Memristors: Resistive switching devices that can store and process information simultaneously
- Transistors: Advanced transistor architectures for synaptic and neuronal emulation
- Neuromorphic Chips: Integrated circuits designed for brain-inspired computing
- Synapse Arrays: Large-scale arrays of programmable synaptic connections
- Neural Networks: Hardware implementations of spiking neural networks
Fabrication Techniques
Our fabrication approach combines traditional semiconductor processes with novel techniques for creating complex neuromorphic architectures. We utilize cleanroom facilities and advanced lithography systems to achieve the precision required for nanoscale device integration.
// Example memristor device simulation
const simulateMemristor = (voltage, currentResistance) => {
const threshold = 0.5; // Switching threshold voltage
const ron = 100; // Low resistance state
const roff = 10000; // High resistance state
if (Math.abs(voltage) > threshold) {
const newResistance = voltage > 0 ? ron : roff;
return {
resistance: newResistance,
state: voltage > 0 ? 'low' : 'high',
conductance: 1 / newResistance
};
}
return { resistance: currentResistance, state: 'stable' };
};
Materials Systems
- Oxide Materials: Transition metal oxides for resistive switching memristors
- 2D Materials: Graphene and TMDs for synaptic transistors
- Organic Semiconductors: Flexible materials for bio-compatible interfaces
- Nanocomposites: Hybrid materials for enhanced device performance
- Phase Change Materials: Chalcogenides for multi-state memory devices
Applications
- Artificial Intelligence: Energy-efficient machine learning hardware
- Brain-Computer Interfaces: Direct neural signal processing
- Sensor Networks: Distributed intelligence for IoT applications
- Robotics: Real-time control systems with learning capabilities
- Medical Devices: Implantable systems for neural prosthetics
Characterization Methods
- Electrical Testing: I-V characteristics and switching dynamics
- Microscopy: SEM, TEM, and AFM for structural analysis
- Spectroscopy: XPS, Raman, and PL for material composition
- Device Testing: Reliability, endurance, and retention measurements
- Neural Network Evaluation: Performance benchmarking on AI tasks
Research Directions
- 3D Integration: Vertical stacking for higher device density
- Multi-functional Devices: Combining memory and logic functions
- Bio-interfacing: Direct integration with biological systems
- Energy Efficiency: Ultra-low power neuromorphic computing
- Scalability: Large-scale fabrication of neuromorphic systems