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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.