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TRL 2 Architecture • PE-ALD AlN/TiN & CUDA BTE

Unlocking Nanoscale Heat Dissipation in GaN-on-Diamond Semiconductors

Eliminating the thermal boundary bottleneck in next-generation RF radar, aerospace, and AI hardware through GPU-accelerated Boltzmann transport modeling and ALD interfacial engineering.

≤ 1.8×10⁻⁸
Target TBR (m²K/W)
> 8.0×
CUDA Speedup vs CPU
3.5nm / 1.0nm
PE-ALD AlN/TiN Stack
100mm
Wafer Integration Target
PRODUCT A • SOFTWARE EDA IP

CUDA 3D BTE Physics Engine

Proprietary GPU-accelerated sparse matrix solver engineered for ballistic and diffusive nanoscale phonon simulation. Implements Robin Boundary Condition abstractions to model interfacial temperature jumps without fine meshing penalties.

• Accelerated via CUDA SDK, CuPy & C++ Kernels
• Robin BC: -κ · (∂T / ∂n) = (1 / TBR) · ΔT
• Direct Export Plugin for EDA Industrial Suites
PRODUCT B • HARDWARE PROCESS IP

PE-ALD Sub-10nm Interlayer Recipe

Graduated Plasma-Enhanced Atomic Layer Deposition (PE-ALD) AlN/TiN (3.5 nm / 1.0 nm) nucleation sequence protecting the GaN buffer from plasma degradation while matching acoustic impedance to CVD Diamond.

• Prevents GaN surface amorphization & oxidation
• Validated via TDTR Laser Characterization
• Fabless Licensing & Royalty Model
lgdcf_bte_cuda_kernel.py
CUDA Accelerated
# LGDCF BTE CUDA Solver Initialization & Boundary Conditions
import cupy as cp

K_GAN = 130.0       # W/m-K (GaN Buffer)
K_DIAMOND = 2000.0  # W/m-K (CVD Substrate)
TBR_TARGET = 1.5e-8 # m^2*K/W Effective Interfacial Resistance

def apply_robin_boundary_condition(A_sparse, T_gan, T_diamond):
    flux_coefficient = 1.0 / TBR_TARGET
    return cp.sparse.linalg.spsolve(A_sparse, flux_coefficient * (T_gan - T_diamond))

// TARGET MARKETS & APPLICATIONS

Commercial Integration Focus

DEFENSE & RADAR
AESA Transceivers
High power-density GaN HEMT modules.
AEROSPACE
SatCom Amplifiers
Ka/Ku band thermal management.
AI HARDWARE
Power Delivery ICs
Sub-10nm package heat extraction.
METROLOGY
TDTR Validation
Optical thermal characterization.