Design

Explanation of Circuit Structure and Simulation Setup

In this project, we focus on optimizing the performance of an analog circuit, specifically a two-stage operational amplifier (OpAmp), using reinforcement learning (RL). The objective is to automate the adjustment of circuit parameters, such as MOS transistor sizing and compensation capacitors, to meet predefined specifications.

This section will explain how the circuit parameters are mapped into the reinforcement learning environment and how the simulation setup is configured to evaluate the performance of the circuit.

3.1 Code Structure for Circuit Design

The project is structured to facilitate circuit optimization using reinforcement learning. The key components are:

• Circuit Parameters as Actions: The action space of the RL agent consists of parameters that control the design of the circuit, including:
  • MOS transistor sizes (W/L for transistors M1, M2, M3, M4, M5, M6)
  • Compensation capacitor $C_c$

  These parameters are adjusted during training by the RL agent.

• Spectre Simulation as Environment: The circuit’s behavior is simulated using Spectre, where the environment interacts with the RL agent by providing feedback on the circuit’s performance. The simulation is triggered each time the agent selects a new set of parameters, and performance metrics are evaluated.

3.2 Simulation Setup

The simulation is configured using the netlist format, which is a text-based representation of the circuit. The RL agent modifies specific parameters in the netlist file, runs the simulation, and extracts performance metrics.

Key components of the simulation setup include:

1. Netlist Templates: The base netlist template defines the circuit structure, including the components (MOS transistors, capacitors, resistors) and their connections. The agent modifies this netlist by adjusting transistor dimensions (W/L) and compensation capacitor values.

2. Performance Metrics: The output of the simulation is used to calculate several key performance indicators:
   • Gain $A_v = \frac{V_{out}}{V_{in}}$
   • Bandwidth $f_{3dB}$
   • Phase Margin $PM$

   These metrics serve as the basis for evaluating the agent’s performance and determining the reward.

3. Reward Function: The reward function is designed to incentivize the agent to optimize for desired characteristics. For example:

   \[R = w_1 \cdot \text{Gain} + w_2 \cdot \text{PM} + w_3 \cdot \text{Bandwidth} - w_4 \cdot \text{Power}\]

   where $w_1$, $w_2$, $w_3$, $w_4$ are weights for each objective.

3.3 Code Workflow for Circuit Optimization

1. Initial Setup: The RL agent starts with an initial set of circuit parameters, which are represented in the form of a netlist.

2. Simulation: The agent interacts with the environment by running simulations. Based on the action taken (the parameter set chosen by the agent), the simulation is executed using NGSpice.

3. Performance Evaluation: The output from the simulation is used to evaluate the circuit’s performance. Metrics such as gain, phase margin, and bandwidth are calculated.

4. Reward Calculation: Based on the calculated metrics, a reward is computed, which informs the agent whether the current design is better or worse than the previous one.

5. Parameter Adjustment: The agent adjusts the circuit parameters (transistor sizes and compensation capacitors) based on the feedback from the reward, and the process repeats iteratively.

6. Convergence: Over time, the RL agent converges to a set of circuit parameters that optimize the performance metrics according to the desired specifications.