Build a systematic analog IC design workflow using the gm/ID methodology, Python automation, lookup tables and practical OTA design techniques.
This practical Analog IC Design Using Python training helps engineers move from transistor-level design intuition to repeatable, data-driven circuit sizing and optimisation. Across three focused sessions, participants learn how to interpret gm/ID design curves, use Python and lookup tables (LUTs) for transistor sizing, and apply the methodology to operational transconductance amplifier (OTA) design.
Build a More Systematic Analog IC Design Workflow
Traditional analog design can involve repeated manual calculations, simulation sweeps and iterative transistor resizing before an acceptable operating point is found.The gm/ID methodology provides a more structured approach by helping engineers understand transistor behaviour across inversion regions and relate device operating points directly to design goals.When this methodology is combined with Python and lookup tables, engineers can make design exploration more systematic and repeatable. Instead of treating each transistor-sizing decision as an isolated iterative exercise, designers can use characterised device data to examine relationships between transconductance efficiency, gain, speed, current density and geometry.
This training develops that workflow progressively. Participants begin with gm/ID design intuition and important transistor design curves before moving into Python-based lookup-table analysis and automated sizing. The final session applies those techniques to practical OTA design and optimisation
Course Overview
The Analog IC Design Using Python Training is a three-session programme for engineers who want to combine established analog design principles with a more systematic, data-driven workflow.The programme begins with the fundamentals of the gm/ID methodology, including inversion regions and important design relationships such as gm/ID, gm/gds, fT and ID/W.Participants then move into a Python-based workflow using lookup tables to support transistor sizing and design-space exploration. The course introduces tools including Jupyter, pygmid, SciPy and Matplotlib within the Python workflow.The final stage applies the methodology to practical analog building blocks, with particular focus on 5T OTA and folded-cascode OTA design, transistor sizing and optimisation across bandwidth, power and area.
For current course access and registration options, visit Alpinum’s Training page.
Course at a glance
| Area | Details |
|---|---|
| Delivery | Live, instructor-led online training |
| Structure | 3 progressive training sessions |
| Duration | 3 × 2-hour sessions · 6 hours total |
| Core methodology | gm/ID-based analog IC design |
| Automation approach | Python-based design exploration and transistor sizing |
| Data workflow | Lookup tables (LUTs) |
| Key design curves | gm/ID, gm/gds, fT and ID/W |
| Python tools | Jupyter, pygmid, SciPy and Matplotlib |
| Circuit focus | Analog building blocks and OTAs |
| OTA architectures | 5T OTA and folded-cascode OTA |
| Optimisation focus | Power, speed, gain, bandwidth and area |
What You Will Learn
By completing this training, participants will develop a structured understanding of how the gm/ID methodology and Python can support practical analog IC design.
- Understand the core principles of the gm/ID design methodology
- Analyse MOS transistor behaviour across different inversion regions
- Interpret important analog design curves including gm/ID, gm/gds, fT and ID/W
- Understand the relationships between power, speed, gain and area
- Identify appropriate transistor operating regions for design objectives
- Use Python for transistor analysis and sizing
- Understand and work with lookup tables (LUTs)
- Replace repeated manual SPICE sweeps with LUT-based design exploration where appropriate
- Use Python tools to make transistor sizing more repeatable
- Modify gm/ID targets and analyse their impact on design choices
- Map circuit-level requirements to transistor-level parameters
- Apply gm/ID methodology to analog building blocks
- Understand 5T OTA design principles
- Work through folded-cascode OTA design considerations
- Size OTA transistors using Python-supported workflows
- Explore trade-offs between bandwidth, power and area
- Apply the overall workflow to practical analog circuit optimisation
Who Should Attend?
This training is designed for engineers and technical learners who want a more systematic approach to transistor sizing and analog circuit design. It is particularly relevant for:
- Analog IC Design Engineers working with CMOS circuit design
- Early-career Analog Engineers developing transistor-level design intuition
- Experienced Analog Designers who want to adopt the gm/ID methodology
- Mixed-Signal Engineers who need stronger analog design understanding
- IC Design Engineers interested in Python-assisted design exploration
- Engineers using SPICE-based workflows who want to understand LUT-based alternatives for design exploration
Technical professionals and advanced learners interested in practical analog design automation.
Looking for the next course date?
Upcoming AMS training dates and current registration options are maintained on our main Training page.
Why gm/ID Methodology Matters in Analog IC Design
Analog transistor sizing requires engineers to balance competing design objectives. Increasing performance in one area can affect another: greater speed may increase current demand, higher gain may introduce different device-sizing constraints, and area or power targets can restrict the available design space.The gm/ID methodology gives designers a systematic way to reason about these trade-offs. The ratio of transconductance to drain current provides insight into transistor efficiency and helps connect the device operating region with circuit-level requirements.Rather than relying only on threshold-voltage-based categorisation or repeated trial-and-error simulation, engineers can use characterised device data to explore operating points across weak, moderate and strong inversion.
This is particularly useful when gm/ID is examined alongside complementary quantities such as gm/gds for intrinsic gain behaviour, fT for speed-related considerations, and ID/W for current density and device sizing.
From Design Intuition to Python-Assisted Analog Design
1. Understand transistor behaviour
Develop intuition around inversion regions, gm/ID and the device characteristics that influence analog performance.
2. Convert device data into a reusable workflow
Use lookup tables and Python to interrogate characterised transistor data rather than repeatedly rebuilding the same exploration manually.
3. Apply the workflow to real analog circuitry
Translate circuit requirements into transistor parameters and use the methodology to size and optimise practical OTA architectures.
This progression makes the training more than a Python scripting course and more than a gm/ID theory course. The objective is to connect analog design knowledge with an efficient computational workflow.
Course Structure
The programme consists of three progressive sessions, with each stage building toward practical OTA design and optimisation.
Session 1: GM/ID Foundations & Design Intuition
Develop transistor-level intuition before automating the workflow
The first session introduces the gm/ID methodology and the design thinking behind it.
Participants examine why conventional trial-and-error approaches can become inefficient and learn how gm/ID provides a more systematic way to analyse transistor operating points.
The session explores transistor inversion regions and the design curves that help engineers reason about efficiency, gain, speed and current density.
Topics covered
- Motivation for the gm/ID methodology
- Limitations of traditional iterative design approaches
- Understanding transistor inversion regions
- gm/ID design curves
- gm/gds
- Transit frequency, fT
- Current density, ID/W
- Power, speed, gain and area trade-offs
- Guided amplifier-sizing example
- Analysis of gm/ID plots
Practical focus
Develop the design intuition needed to select useful transistor operating regions and understand how device-level decisions affect circuit performance.
Learning outcomes
- Explain the basic gm/ID methodology
- Interpret major analog design curves
- Identify appropriate transistor operating regions
- Understand important analog design trade-offs
Session 2: Python-Based GM/ID Workflow
Move from manual exploration to repeatable LUT-based transistor sizing
The second session takes the gm/ID methodology into a computational design workflow.
Participants learn how Python and lookup tables can be used to work with characterised transistor data, perform repeatable analysis and accelerate sizing decisions.
Rather than performing new SPICE sweeps for every design question, engineers learn how pre-characterised lookup data can support rapid design exploration.
Python tools introduced
- Jupyter
- pygmid
- SciPy
- Matplotlib
Topics covered
- Building a Python-based gm/ID workflow
- Understanding lookup tables
- Accessing characterised device data
- LUT-based transistor analysis
- Replacing repeated SPICE sweeps with lookup-based exploration
- Python-assisted transistor sizing
- Live coding workflow for transistor sizing
- Modifying gm/ID targets
- Observing the effect of operating-point changes
- Automating repetitive analog design calculations
Practical focus
Use Python and LUTs to convert transistor characterisation data into a repeatable sizing and design-exploration workflow.
Learning outcomes
- Use Python for transistor sizing and analysis
- Understand LUT-based gm/ID workflows
- Automate selected parts of analog design exploration
- Use scripting more confidently within an IC design workflow
Session 3: OTA Design & Optimisation
Apply gm/ID and Python to practical analog circuit design
The third session brings the earlier concepts together through Operational Transconductance Amplifier (OTA) design.
Participants move from transistor-level design data to circuit-level requirements, using Python-supported gm/ID techniques to size devices and investigate performance trade-offs.
Topics covered
- 5T OTA fundamentals
- OTA design intuition
- Folded-cascode OTA architecture
- Mapping circuit specifications to transistor parameters
- Guided OTA sizing using Python
- Applying gm/ID to practical circuit design
- Bandwidth optimisation
- Power optimisation
- Area considerations
- Performance trade-off analysis
Practical focus
Connect circuit specifications with transistor operating points and use Python-supported gm/ID workflows to size and optimise practical OTA architectures.
Learning outcomes
- Apply gm/ID methodology to practical circuits
- Size devices for OTA architectures
- Use Python-supported workflows for circuit sizing
- Explore bandwidth, power and area trade-offs
- Understand wider system-level consequences of analog design decisions
Key Analog Design Concepts Covered
gm/ID
The gm/ID ratio expresses transconductance efficiency and provides a useful way to analyse transistor operating points. Within the course, it acts as the central design variable connecting device behaviour with circuit requirements.
gm/gds
The gm/gds relationship provides insight into intrinsic transistor gain. Used alongside gm/ID, it helps designers reason about the gain implications of different operating regions.
Transit Frequency – fT
Transit frequency provides a useful indicator of transistor speed. Evaluating fT alongside transconductance efficiency allows designers to consider speed and power efficiency together rather than independently.
ID/W
Drain current per unit device width provides an important connection between operating condition, current density and physical transistor sizing. This becomes particularly valuable when lookup-table data is used to translate a selected operating point into device dimensions.
Why Use Python for Analog IC Design?
Python does not replace analog design knowledge. Its value in this course is in helping engineers make repetitive data handling, analysis and design exploration more efficient. A Python-based gm/ID workflow can help organise characterised transistor information, interrogate lookup tables, visualise device relationships and translate design targets into sizing decisions.
This allows the engineer to spend more time understanding the design trade-offs and less time manually repeating the same calculations.
Lookup Tables for Transistor Sizing and Design Exploration
Lookup tables provide reusable access to transistor characterisation data across relevant operating conditions. Instead of treating every design question as a completely new device simulation exercise, engineers can interrogate existing characterised data to understand how transistor behaviour changes across the design space. Within the gm/ID workflow, LUTs can be used to examine relationships such as gm/ID -> operating point -> ID/W -> transistor width, while other device quantities can help evaluate gain, speed and parasitic behaviour.
This makes LUT handling an important bridge between device characterisation and practical circuit sizing.
From Circuit Specification to Transistor Sizing
A useful analog design workflow must connect system or circuit targets to device-level decisions. The course therefore progresses beyond plotting gm/ID curves. Participants learn how design requirements can be mapped to transistor parameters and how the resulting operating-point information can support device sizing.
The final OTA session then applies that process at circuit level.
- Circuit requirements
- Performance trade-offs
- Select gm/ID operating region
- Interrogate LUT data
- Determine current density / ID/W
- Calculate transistor sizing
- Simulate and evaluate circuit behaviour
- Refine design
OTA Design and Optimisation
Operational Transconductance Amplifiers provide a useful practical application for the gm/ID methodology because the designer must balance multiple performance requirements simultaneously. The programme introduces 5T OTA fundamentals before progressing into folded-cascode OTA architecture and guided sizing.
Participants consider how transistor parameters connect with circuit requirements and explore optimisation across bandwidth, power, area, gain and device operating conditions.
Practical Python and LUT Workflow Examples
Alpinum’s existing training tools roadmap reinforces this course with related Python-based analog design examples.
- Python environment and LUT generation
- NMOS LUT-based characterisation
- Parasitic capacitance Cgg plotting
- Moving from specification to transistor width using LUT data
- Extracting ID/W and calculating transistor width
- Plotting parasitic capacitance, Vdssat and Vov
Designing input pairs for a fully differential folded-cascode OTA
How each module works
Training Format
Three Progressive Sessions
The programme is organised into three connected stages covering design intuition, Python-assisted workflow development and practical OTA application.
Six Hours Total
The full programme provides 3 x 2-hour sessions, giving six hours of focused technical training.
Guided Technical Examples
The course uses worked examples to connect device-level design curves with transistor sizing and practical analog circuitry.
Practical Design Exploration
Participants work with gm/ID concepts, lookup tables and Python-supported design workflows rather than treating the methodology purely theoretically.
Key Benefits
By completing the programme, engineers can develop a more structured approach to analog design and sizing. Key benefits include:
- Build stronger intuition around transistor operating regions
- Understand the gm/ID methodology in practical design terms
- Interpret gm/ID, gm/gds, fT and ID/W curves
- Make more systematic transistor-sizing decisions
- Use Python to support analog design analysis
- Understand LUT-based device exploration
- Reduce reliance on repetitive manual design exploration
- Connect circuit requirements with transistor parameters
- Apply gm/ID methodology to OTA design
- Explore performance trade-offs more efficiently
- Develop a repeatable workflow for future analog designs
Analog IC Design Using Python vs Traditional Trial-and-Error Sizing
The objective of this course is not to remove engineering judgement. It is to give that judgement a more structured foundation. A trial-and-error approach can involve selecting dimensions, simulating, checking performance and repeatedly changing device sizes until specifications are approached.
A gm/ID and LUT-based workflow instead encourages the designer to:
- Understand the required circuit performance
- Select appropriate transistor operating regions
- Use characterised device relationships to support sizing
- Evaluate trade-offs before committing to dimensions
- Use simulation to validate and refine the resulting design
Python helps make the data-analysis and exploration stages more repeatable. The result is a workflow in which simulation remains essential, but the decisions entering simulation are better informed.
How This Course Fits into Alpinum’s Analog & Mixed-Signal Training Path
Analog IC Design Using Python focuses on device-level design methodology, transistor sizing, Python automation, LUTs and OTA optimisation.
AMS Co-Simulation (RNM & UVM)
Focused on real-number modelling and UVM-based mixed-signal verification workflows.
Verilog-AMS, SystemVerilog-AMS & UVM-AMS
Focused on mixed-signal modelling languages and AMS verification methodology.
AMS Co-Simulation (Power-Aware / UPF)
Focused on low-power mixed-signal verification where analog behaviour, digital control and explicit power intent interact.
Related Analog IC Design Expertise
Alpinum also supports semiconductor engineering teams with analog and mixed-signal design, modelling, simulation and verification services. Its existing analog capability includes gm/ID-based design, Python environments and LUT generation, LUT handling and curve extraction, Python-driven sizing insight and analog circuit project work.
Frequently Asked Questions
Move from Design Intuition to a Repeatable Python-Assisted Analog Workflow
Build practical capability across gm/ID methodology, transistor design curves, lookup-table analysis, Python-assisted sizing and OTA optimisation.
