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. 

Update 4.0: The software now uses LUA scripting to run the routine directly inside the sourcemeter for the synapse analysis (that would be overkill for solar cells and transistors anyway). This means that for synapses, there is a gain a speed by a factor nearly 100. On a Keithley 2636A, I managed to get accurate voltage pulses of about 0.1ms, while previous versions started becoming inaccurate around 10ms.

Update 4.2: Added the selfpowered synapse mode, where the photocurrent is read at each stimulation pulses in short circuit conditions (zero power). Fixed an issue with the LAN connection on some instruments.

Update 4.2.2: Fixed a connection bug in LAN. Fixed a bug when exporting data with a Japanese computer. Why do Japanese have to constantly do things differently?

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