Keithley Dual SMU Parameter Analyzer - Software Description

A Python-based graphical interface for electrical characterization using Keithley 26xx dual-channel sourcemeters. This software enables automated measurement and analysis of photovoltaic devices, transistors, and neuromorphic/memristive devices with advanced data processing and visualization capabilities.

Core Functionalities

1. Current-Voltage (IV) Characterization

• Automated JV sweeps with configurable voltage range, step size, and measurement speed (NPLC)
• Multi-curve overlay plotting for comparative analysis
• Dual y-axis visualization showing current density and power density simultaneously
• Semilog plotting mode for analyzing devices across wide current ranges
• Hysteresis measurement with configurable forward/reverse sweep cycles
• Dark and illuminated measurements with photovoltaic parameter extraction

2. Photovoltaic Device Analysis

• Automatic PV parameter extraction: Open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and power conversion efficiency (PCE)
• Interpolation-based Voc calculation for improved accuracy
• Configurable irradiance settings (W/m²) for standardized testing
• Batch processing with multi-sample storage and CSV export

3. Transistor Characterization

• Automated gate-voltage sweep measurements for OFET/TFT devices
• Dual-channel operation with independent gate and drain control
• Output characteristics generation with multi-Vgs curve families
• Transfer curve analysis with data export capabilities

4. Neuromorphic Synapse Characterization

• Pulse-read sequences for electrical, optical, and memristor-based synapses
• Configurable stimulus parameters: voltage/current drive, pulse width, period, and amplitude
• Real-time conductance monitoring across pulse trains
• Synaptic metrics calculation: Paired-pulse facilitation (PPF), conductance change (ΔG), potentiation/depression quantification
• Safety checks for high-voltage operations

5. Spike-Rate-Dependent Plasticity (SRDP)

• Frequency sweep characterization (linear or logarithmic scaling)
• Rate-dependent learning curves showing ΔG vs. spike frequency
• Configurable frequency range (0.1 Hz - 1 kHz+)
• Multi-point analysis with automated data collection

6. Spike-Timing-Dependent Plasticity (STDP)

• Timing-dependent plasticity window measurement
• Pre-post spike pair generation with precise Δt control
• Bidirectional plasticity characterization (LTP/LTD regions)
• STDP curve plotting with automatic LTP/LTD region annotation

7. Simulation Mode

• Hardware-free testing with physics-based device models
• Exponential conductance change simulation for memristive behavior
• Realistic noise injection for measurement validation
• SRDP and STDP simulation engines for protocol development

8. Instrument Communication

• Multi-interface support: GPIB, RS-232, and LAN/Ethernet
• Automatic timeout management based on measurement parameters
• 4-wire and 2-wire sensing modes
• Current compliance protection (100 nA to 1.5 A range)
• Autorange capabilities for current measurement

9. Data Management

• CSV export with embedded metadata headers
• Multi-sample batch storage with unique cell identifiers
• Derived metrics calculated and stored automatically
• Timestamp tracking for temporal analysis
• Parameter presets for common measurement protocols (LTP, LTD, PPF, high-speed)

10. User Interface

• Modern CustomTkinter GUI with scrollable parameter panels
• Real-time plotting using Matplotlib with dual-axis support
• Parameter display panel showing calculated metrics live
• Preset selector for rapid protocol switching
• Measurement mode tabs: Diode, Transistor, Synapse, SRDP, STDP

Technical Specifications

• Programming Language: Python 3.x
• Key Dependencies: PyVISA, CustomTkinter, Matplotlib, NumPy
• Target Hardware: Keithley 2636A/B Dual-Channel SMU
• Communication Protocols: GPIB (IEEE-488), RS-232, TCP/IP
• Data Format: CSV with metadata headers
• Measurement Resolution: 0.1-10 NPLC (power line cycles)

Use Cases

• Memristor and resistive RAM device testing
• Artificial synapse characterization for neuromorphic computing
• Organic and perovskite solar cell characterization
• Organic field-effect transistor (OFET) analysis
• Optoelectronic device measurements
• Solar Cells

Author: Zacharie Jehl Li-Kao
Contact: zacharie.jehl@upc.edu

Source code: https://github.com/SOLIS-project