Transistor Database

Comprehensive Device Library

Our transistor database includes detailed specifications, switching characteristics, and thermal properties for a wide range of power semiconductor devices.

Wide-Bandgap Devices

Silicon Carbide (SiC) and Gallium Nitride (GaN) transistors with comprehensive switching loss data and thermal models.

Traditional Semiconductors

IGBT and MOSFET libraries with datasheet parameters and validated performance models.

Mission Profile Analysis

Device selection optimised for your specific operating conditions, efficiency targets, and thermal constraints.

What a device record contains

A part number and a few headline numbers are not enough to size a converter. Every entry is stored as curves and waveforms — the form a loss model, a thermal solver, or an AI design agent can actually consume.

Double-pulse test waveform (raw record)

Every device carries measured waveforms, not just a number. Full v/i captures at a stated DC-link voltage, gate resistance, loop inductance and junction temperature give integrated E_on and E_off plus dv/dt, di/dt, overshoot and ringing frequency. A single datasheet energy figure does not transfer to your commutation loop — a waveform does.

Output characteristics

Source: digitised datasheet families plus curve-tracer measurements. Each record stores I_D–V_DS families at 25 / 125 / 175 °C, from which R_DS(on), transconductance and de-saturation thresholds are extracted.

Junction capacitance vs. bias

Why it is stored as a curve: integrating C_oss(V) gives Q_oss and E_oss, which set resonant commutation time, ZVS dead-time and whether zero-voltage switching closes at all. C_rss sets dv/dt immunity and cross-talk margin.

Switching energy vs. current and T_j

This is what a loss model consumes. E_on and E_off are stored as a four-dimensional lookup over (I, V_DC, T_j, R_g), so a mission-profile efficiency and junction-temperature iteration runs directly off the table instead of re-simulating every operating point.

Transient thermal impedance

Stored as a Foster/Cauer network, not a single number. Each package carries R–C ladder parameters that drop straight into an electro-thermal co-simulation, so junction-temperature swing ΔT_j can be solved cycle by cycle over a mission profile — and ΔT_j is exactly what a lifetime model needs.

Figures are illustrative of the stored data format. Background reading: double-pulse testing · static characterisation · gate charge & capacitance · transient thermal impedance.

Parameter coverage

Fields carried per device, with the test conditions attached. A value without its conditions is not data — so bias, temperature, gate drive and loop inductance travel with every number.

Static & package

V_DS / V_CES blocking voltage
R_DS(on)(T_j) mΩ, normalised curve
I_D–V_DS families per V_GS, per T_j
V_th, g_fs with hysteresis where relevant
Body / freewheel diode V_F, Q_rr, t_rr
Package & footprint creepage, stray L, kelvin source

Dynamic

C_iss, C_oss, C_rss(V) full curve, not a point
Q_g, Q_gd, Q_oss, E_oss integrated from curves
E_on, E_off vs I, V_DC, T_j, R_g
dv/dt, di/dt measured, per gate resistor
Overshoot & ringing with stated loop inductance
Raw DPT waveforms CSV, re-analysable

Thermal & reliability

R_th(j-c), R_th(j-a) K/W, stated boundary
Z_th network Foster / Cauer R–C ladder
SOA & short-circuit withstand time, energy
Power-cycling data ΔT_j vs cycles-to-fail
Derating rules voltage, current, temperature
Model links SPICE, PLECS, ANN surrogate

SiC MOSFET
SiC JFET / cascode
GaN HEMT (e-mode)
GaN cascode
Si MOSFET
Si superjunction
IGBT (discrete)
IGBT module
Reverse-conducting & RB devices
SiC Schottky diode

How it feeds a design

Shortlist under constraints

Filter by blocking voltage, current, package and cost, then rank by computed loss at your operating point rather than by datasheet headline figures.

Close the electro-thermal loop

Switching-energy tables and Z_th networks solve together, so junction temperature, loss and derating converge instead of being assumed.

Feed the agent

The same records are exposed to AI-assisted design workflows, so a device choice comes back with the conditions and measurements that justify it.

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