Nanowire IV Data Analysis: Threshold Voltage, Contact Resistance & Mobility

Author: Mykola Chernyashevskyy
Affiliation: University of Pittsburgh, Department of Physics and Astronomy
Email: [email protected]

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🧪 Purpose

This notebook processes and analyzes current-voltage (IV) data from nanowire transport measurements. It extracts key device parameters such as:

• Threshold Voltage (Vₜ)
• Contact Resistance (Rₛ)
• Field-Effect Mobility (μ)

The analysis supports experiments involving semiconducting nanowires and their electrical properties as a function of gate voltage and bias.

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🗂️ Data Preparation

📁 Data Input

The user is prompted to:

• Select a CSV file containing IVVI measurement output.
• Scale data based on known conversion factors to convert raw units to volts and amperes.

⚠️ Important Notes:

• Python uses 0-based indexing.
• Ensure correct sign conventions when selecting back-gate voltage and current columns.

📸 Reference images are provided in the notebook for selecting the correct scaling factors and data columns.

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⚙️ Capacitance Estimation

• Estimate the gate capacitance using SEM imaging of the nanowire contacts.
• Reference a capacitance chart based on nanowire coating and substrate type (bare, CdTe, SiOx, HfOx).

💡 Capacitance is used in calculating mobility via the fitted current equation.

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📐 Model and Fitting

✅ Fit Equation

The notebook fits current vs. gate voltage to the following quadratic form:

$$ I = \mu C \frac{L}{V_{\text{bias}}} (V_{\text{g}} - V_{\text{th}})^2 + R_c $$

• μ: Carrier mobility
• C: Gate capacitance (estimated)
• L: Contact spacing (from SEM)
• Vbias: Source-drain bias
• Vg: Gate voltage
• Vth: Threshold voltage
• Rc: Contact resistance

📦 Fitting is done using scipy.optimize.curve_fit

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📊 Outputs

• Printed fit parameters:
  • Threshold Voltage (Vth)
  • Contact Resistance (Rc)
  • Mobility (μ)
• Standard deviation errors (via covariance matrix)
• Clean plots with IV traces and fitted curve

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🐼 Additional Tools

• The notebook includes a Pandas block for:
  • Inspecting and summarizing datasets
  • Exporting processed results if needed

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📚 References

• Transport modeling adapted from standard MOSFET theory
• Curve fitting: scipy.optimize.curve_fit
• Data handling: pandas, numpy, matplotlib