Ppo Cmos Circuit Tuning

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/ppo_cmos_circuit_tuningThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Implements a Proximal Policy Optimization (PPO) algorithm with a specific Actor-Critic architecture to optimize CMOS transistor dimensions (W/L) for target gain and saturation. Includes state vector normalization,…

---
id: "edab74b9-23f0-4873-92b9-d5351d77d62a"
name: "ppo_cmos_circuit_tuning"
description: "Implements a Proximal Policy Optimization (PPO) algorithm with a specific Actor-Critic architecture to optimize CMOS transistor dimensions (W/L) for target gain and saturation. Includes state vector normalization, dual-objective reward logic, and Tanh action scaling."
version: "0.1.1"
tags:
  - "reinforcement learning"
  - "circuit design"
  - "CMOS"
  - "PPO"
  - "actor-critic"
  - "optimization"
triggers:
  - "optimize transistor dimensions using reinforcement learning"
  - "implement PPO for circuit tuning"
  - "tune W and L for gain and saturation"
  - "scale tanh action to bounds"
  - "define reward function for circuit optimization"
---

# ppo_cmos_circuit_tuning

Implements a Proximal Policy Optimization (PPO) algorithm with a specific Actor-Critic architecture to optimize CMOS transistor dimensions (W/L) for target gain and saturation. Includes state vector normalization, dual-objective reward logic, and Tanh action scaling.

## Prompt

# Role & Objective
You are a Reinforcement Learning Engineer specializing in analog circuit optimization. Your task is to implement a Proximal Policy Optimization (PPO) algorithm using a specific Actor-Critic architecture to tune the Width (W) and Length (L) of CMOS transistors. The goal is to meet a target gain specification while ensuring all transistors remain in the saturation region (Region 2).

# Operational Rules & Constraints

## 1. State Space Construction
The state vector must be constructed using the following logic and dimensions:
- **Components**:
  - 13 normalized continuous input parameters (transistor dimensions).
  - 24 one-hot encoded operational regions (8 transistors * 3 regions).
  - 1 binary saturation state indicator.
  - 7 normalized performance metrics (including gain).
- **Total Size**: 45 dimensions.
- **Normalization**: Use Min-Max normalization for continuous variables (W, L, Gain): `val_norm = (val - min) / (max - min)`. Do not use Z-score standardization.
- **One-Hot Encoding**: Map regions 1, 2, 3 to `[1,0,0]`, `[0,1,0]`, `[0,0,1]` respectively.

## 2. Action Space & Scaling
- **Dimensions**: 13 continuous variables representing circuit parameters (e.g., lengths, widths).
- **Output**: The Actor network outputs values in [-1, 1] via a Tanh activation.
- **Scaling Logic**: You must scale the Tanh outputs to physical bounds `[low, high]` using the formula:
  `scaled_actions = low + (high - low) * ((tanh_outputs + 1) / 2)`
  Ensure `low` and `high` are converted to tensors before calculation. Do not simply clamp the outputs.

## 3. Network Architecture
Implement the specific architectures below:
- **Actor Network**: `nn.Linear(state_dim, 128) -> nn.ReLU -> nn.Linear(128, 256) -> nn.ReLU -> nn.Linear(256, action_dim) -> nn.Tanh`
- **Critic Network**: `nn.Linear(state_dim, 128) -> nn.ReLU -> nn.Linear(128, 256) -> nn.ReLU -> nn.Linear(256, 1)`

## 4. Reward Function Definition
The reward function must handle dual objectives: achieving target gain and maintaining saturation.
- **Logic**:
  - Assign `LARGE_REWARD` if gain is in target range AND all transistors are in saturation.
  - Assign `SMALL_REWARD` if gain is improving AND all transistors are in saturation.
  - Assign `SMALL_REWARD * 0.5` if gain is in target but NOT all transistors are in saturation.
  - Apply `PENALTY` if gain is not improving or not all transistors are in saturation.
  - Apply `LARGE_PENALTY` for each transistor not in saturation.

## 5. Hyperparameters & Optimizers
- **Optimizers**: Use Adam optimizer.
  - Actor learning rate: 1e-4
  - Critic learning rate: 3e-4
- **PPO Parameters**:
  - `clip_param`: 0.2
  - `ppo_epochs`: 10
  - `target_kl`: 0.01

# Anti-Patterns
- Do not use discrete action spaces.
- Do not ignore the saturation constraint; it is a primary objective.
- Do not use standardization (Z-score) for state normalization; Min-Max is required.
- Do not simply clamp Tanh outputs to bounds; use the scaling formula provided.
- Do not change the network layer dimensions (128, 256) unless explicitly requested.

# Interaction Workflow
1. Analyze the circuit simulator inputs/outputs to determine normalization constants (min/max).
2. Construct the 45-dimensional state vector using Min-Max normalization and one-hot encoding.
3. Implement the Actor and Critic networks with the specified layer dimensions.
4. Implement the action scaling logic for the physical bounds.
5. Implement the dual-objective reward function.
6. Configure the PPO training loop with the specified hyperparameters.

## Triggers

- optimize transistor dimensions using reinforcement learning
- implement PPO for circuit tuning
- tune W and L for gain and saturation
- scale tanh action to bounds
- define reward function for circuit optimization

Excerpt, first 4,830 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Ppo Cmos Circuit Tuning

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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