Official Resources
- Source Repository: https://github.com/Jiezi-negf/Jiezi
- Documentation: Included in repository
- License: Open source
Overview
Jiezi is an open-source Python software for simulating quantum transport of nanoscale devices. It solves the Schrödinger and Poisson equations self-consistently using the non-equilibrium Green's function (NEGF) method with finite element discretization, enabling atomistic-level device simulation.
Scientific domain: Self-consistent NEGF quantum transport, device simulation
Target user community: Researchers simulating quantum transport in nanoscale electronic devices with self-consistent electrostatics
Theoretical Methods
- Non-equilibrium Green's function (NEGF)
- Self-consistent Schrödinger-Poisson solver
- Finite element method (FEM)
- Tight-binding Hamiltonians
- Density matrix calculation
- Open boundary conditions
- Scattering self-energies
Capabilities (CRITICAL)
- Self-consistent NEGF-Poisson simulation
- Quantum transport in nanoscale devices
- I-V characteristics with self-consistent potential
- Transmission function
- Density of states
- Charge density distribution
- Electrostatic potential
- FET and nanowire device simulation
Sources: GitHub repository
Key Strengths
Self-Consistent NEGF:
- Schrödinger-Poisson coupling
- Realistic device electrostatics
- Gate voltage effects
- Charge self-consistency
Finite Element Method:
- Flexible geometry
- Non-uniform meshing
- Accurate potential
- Complex device shapes
Python Framework:
- Easy to use and modify
- Jupyter notebook compatible
- Extensible architecture
- Clear code structure
Inputs & Outputs
-
Input formats:
- Python configuration scripts
- Device geometry parameters
- Material parameters
-
Output data types:
- I-V characteristics
- Transmission spectra
- Charge density
- Electrostatic potential
- Density of states
Interfaces & Ecosystem
- Python: Primary language
- NumPy/SciPy: Numerical computation
- Matplotlib: Visualization
- FEM: Finite element discretization
Performance Characteristics
- Speed: Moderate (self-consistent iteration)
- Accuracy: Good with converged Hamiltonian
- System size: Thousands of atoms
- Memory: Moderate to high
Computational Cost
- Single bias point: Minutes to hours
- Full I-V: Hours to days
- Typical: Moderate to expensive
Limitations & Known Constraints
- Tight-binding only: No DFT integration
- Python speed: Slower than compiled codes
- Documentation: Limited
- Small community: Research code
Comparison with Other Codes
- vs Transiesta: Jiezi is Python/TB, Transiesta is DFT+NEGF
- vs Nanodcal: Jiezi is open source, Nanodcal is commercial LCAO
- vs Smeagol: Jiezi is standalone Python, Smeagol is SIESTA-based
- Unique strength: Self-consistent NEGF-Poisson with FEM, Python framework for device simulation
Application Areas
Nanoscale FETs:
- Nanowire FET simulation
- Gate voltage effects
- Subthreshold swing
- ON/OFF current
Molecular Devices:
- Molecular junction I-V
- Gate-controlled transport
- Single-molecule transistors
- Switching behavior
2D Material Devices:
- Graphene nanoribbon FETs
- MoS2 transistor simulation
- Heterostructure devices
- Contact resistance
Best Practices
Self-Consistent Convergence:
- Monitor charge convergence
- Use appropriate mixing
- Start from zero-bias solution
- Check Poisson convergence
Device Setup:
- Use realistic geometry
- Include sufficient lead length
- Appropriate boundary conditions
- Validate against analytical models
Community and Support
- Open source on GitHub
- Research code
- Limited documentation
- Example calculations provided
Verification & Sources
Primary sources:
- GitHub: https://github.com/Jiezi-negf/Jiezi
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: Included in repository
- Active development: Research code
- Specialized strength: Self-consistent NEGF-Poisson with FEM, Python device simulation