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Transistor Models

Community
Arcadia-1
transistor-models

Complete PTM (Predictive Technology Model) MOSFET model library from mec.umn.edu/ptm, covering all nodes: bulk conventional 180/130/90/65nm, bulk HP/LP 45/32/22nm (BSIM4), and PTM-MG multi-gate FinFET 7/10/14/16/20nm (BSIM-CMG, HP + LSTP). No manual downloads required after installing this skill. Independent of the gmoverid skill — can be used directly in any ngspice/HSPICE project.

Overview

PublisherArcadia-1
Repositorygmoverid-skill
Skill nametransistor-models
Stars
139
Forks
28
Bundled files
33
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 33 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Arcadia-1 on GitHub. Read the source before you install it.

Installation

Install the Transistor Models AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Arcadia-1/gmoverid-skill/tree/main/transistor-models
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Arcadia-1/gmoverid-skill.git /tmp/gmoverid-skill
mkdir -p .claude/skills
cp -r /tmp/gmoverid-skill/transistor-models .claude/skills/transistor-models
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Transistor Models in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Transistor Models on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Transistor Models is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Transistor Models Skill — Complete PTM Library

Important — do not modify skill files during normal use. When using models, copy the required .lib files into the user's project directory (outside .claude/). Do not modify or delete files inside the skill's assets/models/ directory. Only update the skill's internal files when the user explicitly asks to refresh the model library.

Source: All model files are from PTM — Arizona State University (ptm.asu.edu), free for academic research.

Citations:

  • Bulk CMOS nodes: W. Zhao and Y. Cao, "New Generation of Predictive Technology Model for Sub-45 nm Early Design Exploration," IEEE Trans. Electron Devices, vol. 53, no. 11, pp. 2816–2823, Nov. 2006. doi: 10.1109/TED.2006.884077
  • PTM-MG FinFET nodes: S. Sinha, G. Yeric, V. Chandra, B. Cline and Y. Cao, "Exploring sub-20nm FinFET design with Predictive Technology Models," DAC 2012, pp. 283–288. doi: 10.1145/2228360.2228414

Directory Structure

assets/models/
├── bulk_cmos/              — Bulk CMOS (BSIM3v3 / BSIM4)
│   ├── ptm180.lib          — 180nm conventional
│   ├── ptm130.lib          — 130nm conventional
│   ├── ptm90.lib           — 90nm conventional
│   ├── ptm65.lib           — 65nm conventional
│   ├── ptm45hp.lib         — 45nm HP
│   ├── ptm45lp.lib         — 45nm LP
│   ├── ptm32hp.lib         — 32nm HP
│   ├── ptm32lp.lib         — 32nm LP
│   ├── ptm22hp.lib         — 22nm HP
│   └── ptm22lp.lib         — 22nm LP
└── finfet/
    ├── models              — PTM-MG library entry point (.LIB ptm{n}hp / ptm{n}lstp)
    ├── param.inc           — Shared parameters (fin_height/fin_width/lg/vdd, per node)
    ├── hp/                 — HP nodes: {7,10,14,16,20}nfet.pm / pfet.pm
    └── lstp/               — LSTP nodes: {7,10,14,16,20}nfet.pm / pfet.pm

Model Reference

Bulk CMOS — Conventional (BSIM3v3/BSIM4, combined NMOS+PMOS)

FileNodeVDDModel names
bulk_cmos/ptm180.lib180nm1.8VNMOS / PMOS
bulk_cmos/ptm130.lib130nm1.3Vnmos / pmos
bulk_cmos/ptm90.lib90nm1.2Vnmos / pmos
bulk_cmos/ptm65.lib65nm1.1Vnmos / pmos

Bulk CMOS — HP/LP (BSIM4, combined NMOS+PMOS)

FileNodeTypeVDDModel names
bulk_cmos/ptm45hp.lib45nmHP1.0Vnmos / pmos
bulk_cmos/ptm45lp.lib45nmLP1.1Vnmos / pmos
bulk_cmos/ptm32hp.lib32nmHP0.9Vnmos / pmos
bulk_cmos/ptm32lp.lib32nmLP1.0Vnmos / pmos
bulk_cmos/ptm22hp.lib22nmHP0.8Vnmos / pmos
bulk_cmos/ptm22lp.lib22nmLP1.0Vnmos / pmos

FinFET — PTM-MG Multi-Gate (BSIM-CMG)

Entry file: finfet/models — select node and type via .lib section tag:

TagNodeTypeVDD
ptm20hp20nmHP0.9V
ptm16hp16nmHP0.85V
ptm14hp14nmHP0.8V
ptm10hp10nmHP0.75V
ptm7hp7nmHP0.7V
ptm20lstp20nmLSTP0.9V
ptm16lstp16nmLSTP0.85V
ptm14lstp14nmLSTP0.8V
ptm10lstp10nmLSTP0.75V
ptm7lstp7nmLSTP0.7V

FinFET model names are nfet / pfet (subckt-wrapped); use NFIN to set the number of fins.


ngspice Usage

Loading a Bulk CMOS File

spice
* Load 45nm HP (contains both nmos and pmos models)
.include "models/bulk_cmos/ptm45hp.lib"

* Instantiate NMOS
M1  drain  gate  source  bulk  nmos  W=1u  L=45n

Loading a PTM-MG FinFET

spice
* Load 7nm HP FinFET
.lib "models/finfet/models" ptm7hp

* PTM-MG model names are nfet / pfet
* Use NFIN (number of fins) instead of W
M1  drain  gate  source  bulk  nfet  NFIN=1  L=7n

Model Type Quick Reference

Modelngspice LEVELApplicable nodesNotes
BSIM3v38180nm–250nmMature bulk CMOS
BSIM45465nm–22nmBulk HP/LP
BSIM-CMG727nm–20nmFinFET / multi-gate

Key Parameter Quick Reference

ParameterMeaningTypical range
vth0Nominal threshold voltage [V]NMOS: 0.3–0.5; PMOS: −0.5 to −0.3
toxeEquivalent gate-oxide thickness [m]1–4 nm (deep submicron)
u0Low-field mobility [m²/Vs]NMOS: 0.03–0.05; PMOS: 0.01–0.02
vsatSaturation velocity [m/s]1e5–2.5e5

Full parameter table in references/model_params.md.


Using a Custom PDK Model

If you have your own .lib file (foundry PDK or measured):

  1. Copy it into the project models/ directory.
  2. Find the .model name inside the file (e.g. .model mynmos NMOS level=54).
  3. Instantiate using that name: M1 d g s b mynmos W=... L=...
  4. Configure sweep ranges based on vth0, tox, etc.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Transistor Models AI skill do?

Complete PTM (Predictive Technology Model) MOSFET model library from mec.umn.edu/ptm, covering all nodes: bulk conventional 180/130/90/65nm, bulk HP/LP 45/32/22nm (BSIM4), and PTM-MG multi-gate FinFET 7/10/14/16/20nm (BSIM-CMG, HP + LSTP). No manual downloads required after installing this skill. Independent of the gmoverid skill — can be used directly in any ngspice/HSPICE project.

Why use Transistor Models on TypingMind?

Because you install it once and use it with any model. Transistor Models is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Transistor Models in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Arcadia-1/gmoverid-skill/tree/main/transistor-models. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Transistor Models?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Transistor Models?

As many as you like. As long as a model supports skills, you can use Transistor Models with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Transistor Models AI skill free?

It is published on GitHub by Arcadia-1. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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