Calculate thermal fatigue life of Au, Al, and Cu bond wires using the Coffin-Manson law. Adjust CTE mismatch, temperature swing, and wire geometry for real-time Nf calculation and material comparison.
Parameters
Wire Material
Wire Diameter d
µm
Wire Span L
mm
Temp. Swing ΔT
°C
Cycles per Day
/day
CTE Mismatch Δα
ppm/K
Results
Calculating...
Δε = Δα × ΔT
Nf = C / (Δε)^n
Au: C=0.5, n=2.0
Al: C=0.3, n=2.2
Cu: C=0.4, n=2.1
Results
Thermal Strain Δε
—
Cycles to Fail Nf
—cyc
Estimated Life
—yr
Failure Mode
—
Nf vs ΔT Comparison by Material
Schematic bond-wire loop shape (scaled by wire diameter and span)
Theory & Key Formulas
$$N_f = C \cdot (\Delta\varepsilon_{pl})^{-m} \quad (\text{Coffin-Manson 則})$$
疲労寿命 N_f [サイクル]。Δε_pl：塑性ひずみ振幅（無次元）、C・m：材料定数
$$\Delta\varepsilon = \alpha_{CTE,mismatch} \cdot \Delta T \cdot L_s / D_w$$
What exactly is bond wire fatigue, and why does it happen in electronics?
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Basically, it's the cracking and eventual failure of the tiny wires connecting a silicon chip to its package. It happens because the chip and the package expand and contract at different rates when the temperature changes—this is the CTE mismatch you see in the simulator. Each temperature cycle bends the wire a little, causing fatigue.
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Wait, really? So the "Temp. Swing" slider directly controls how much bending happens? How do we predict when it will fail?
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Exactly. A larger ΔT means more bending strain. To predict failure, engineers use the Coffin-Manson law, an empirical model for low-cycle fatigue. In this simulator, it calculates the number of cycles to failure, $N_f$, based on the plastic strain amplitude in the wire from that bending. Try increasing the "CTE Mismatch" and watch the predicted life drop dramatically.
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That makes sense. So what's the practical effect of choosing a different "Wire Material" like Au vs. Al? Is it just about cost?
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Not just cost! Gold (Au) is more ductile—it can handle more plastic deformation per cycle without cracking, which gives it a much longer fatigue life. Aluminum (Al) is stiffer and cheaper but fails sooner. Copper (Cu) is in between. Change the material in the simulator and see how the life changes, even with all other parameters like "Wire Diameter" and "Span" held constant.
Physical Model & Key Equations
The core model estimates the plastic shear strain amplitude in the bond wire caused by thermal expansion mismatch. The wire is treated as a beam fixed at both ends, forced to deflect as the distance between anchor points changes with temperature.
$$ \Delta \gamma_{pl}= \frac{\Delta \alpha \cdot \Delta T \cdot L}{d}$$
Where:
$\Delta \gamma_{pl}$ = Plastic shear strain amplitude
$\Delta \alpha$ = CTE mismatch between chip and package (ppm/°C)
$\Delta T$ = Temperature swing (°C)
$L$ = Wire span (length between bonds) (mm)
$d$ = Wire diameter (mm)
This shows why long, thin wires (high L/d ratio) are most vulnerable—they amplify the strain.
The strain amplitude is then plugged into the Coffin-Manson law to predict the number of cycles to failure.
$$ N_f = \frac{C}{(\Delta \gamma_{pl})^n} $$
Where:
$N_f$ = Cycles to failure
$C$ = Material ductility coefficient (e.g., much higher for Au than Al)
$n$ = Fatigue exponent (typically between 1.5 and 2.5 for metals)
This is a power-law relationship. A small increase in strain causes a large decrease in fatigue life, which is why controlling CTE mismatch and temperature swing is so critical.
Frequently Asked Questions
Wire material (Au/Al/Cu), wire diameter, span length, temperature change range (ΔT), and the CTE (coefficient of thermal expansion) mismatch value at the joint are required. Once these values are entered, the fatigue life (Nf) is calculated in real time based on the Coffin-Manson law.
It displays a comparative view of the fatigue life of Au, Al, and Cu wires under the same conditions. By varying the temperature amplitude or wire diameter, you can visually confirm changes in the life of each material, which aids in selecting the optimal wire material and performing trade-off analysis of design conditions.
C and n are empirical values based on experimental data for each wire material. This tool uses default values set from literature and standard fatigue test results for Au, Al, and Cu, respectively. Users cannot arbitrarily change these values.
Since this tool is based on a simplified model, the actual lifespan is affected by secondary factors such as joint geometry and intermetallic compound growth at the interface. Please use it solely for relative comparisons and trend understanding in the early design stages.
Real-World Applications
Automotive Electronics: Under-hood control modules experience extreme temperature cycles from cold starts to engine heat. Predicting bond wire fatigue life ensures reliability over a 15-year vehicle lifespan. Engineers use tools like this to select appropriate wire materials and design robust packages.
Power Module Reliability: Inverters for electric vehicles and industrial motors switch high currents, creating significant internal heating cycles. Bond wire failure is a primary failure mode. Simulations help optimize wire diameter, loop height, and material to survive millions of power cycles.
Consumer Electronics: Smartphones and laptops heat up during use and cool down when idle. While swings are smaller, the high number of daily cycles can lead to fatigue over time. This analysis informs quality standards and accelerated life testing protocols.
Aerospace & Defense Electronics: Systems must operate reliably across vast temperature ranges, from high-altitude cold to avionics bay heat. Accurate fatigue life prediction is part of the rigorous qualification process, often favoring more expensive but durable gold wires for critical components.
Common Misconceptions and Points to Note
There are a few key points I want you to be especially mindful of when starting to use this tool. First, remember that "the calculation result is not an absolute lifetime." This tool is strictly for "observing trends" based on a simplified one-dimensional model. In reality, many more factors affect a wire's lifespan, such as its loop shape, interference with adjacent wires, and bonding strength. For example, even if the calculation shows a 100,000-cycle life, it's not uncommon for actual products to last only half that due to manufacturing variations or impurity effects. How you incorporate a safety margin becomes crucial in practical work.
Next, misconfiguring the "Temperature Amplitude ΔT" parameter is a common mistake. Please don't simply input something like "the operating temperature range is -40°C to 125°C, so ΔT=165°C." The actual temperature change the wire experiences is the sum of ambient temperature and self-heating. For instance, in a power device, even if the ambient is 85°C, the wire itself might momentarily reach 150°C due to Joule heating during current flow. In that case, ΔT would be 65°C (150-85). Identifying this "effective temperature amplitude" is the first step toward an accurate prediction.
Finally, be wary of blindly trusting material constants. The constants for gold, aluminum, and copper in the tool are representative values, but actual wire characteristics can change significantly with trace additive elements. For example, aluminum-silicon wire with 1% silicon added has higher strength and different fatigue properties compared to high-purity aluminum. After making comparisons with the tool, make it a habit to always consult the "datasheet for the specific material you are using" or "in-house measured data."