Laser Frequency Locking System LOCKED

Cost-Effective Stability via STM32 & Machine Learning

1. Introduction

In quantum physics experiments involving quantum computers or gravimeters, a frequency-stable laser source is indispensable. This project presents a cost-effective frequency locking system using an STM32 microcontroller. Our design minimizes frequency drift to well below the 5.0 MHz linewidth of the Cs atom transition, providing an economical alternative to high-end commercial locking modules.

Locking Box
Autonomous Laser Locking System Enclosure

2. Hardware Architecture

The system integrates optical spectroscopy with high-speed digital electronics. We utilize Dichroic Atomic Vapor Laser Lock (DAVLL) and Saturated Absorption Spectroscopy (SAS) to generate feedback signals.

Optics (DAVLL/SAS)

Generates saturated absorption peaks corresponding to the hyperfine transitions of Cesium atoms using an 852nm ECDL.

SAS Signal
Saturated Absorption Spectrum of Cs Atom

Electronics (STM32 Control)

Reads photodiode signals and implements a PID controller that drives a Piezo chip to actively stabilize the laser's cavity length.

Electronics Setup
Microcontroller-based Control Circuitry

3. Software Intelligence

We developed multiple layers of software intelligence to handle everything from manual peak selection to fully autonomous neural-network-based locking.

Machine Learning: CNN Peak Detection

Identifying lockpoints in noisy environments is a major challenge. We leveraged Convolutional Neural Networks (CNNs) to autonomously analyze signal patterns and identify resonance centers with high robustness, even in non-ideal signal-to-noise conditions.

CNN Architecture
Neural Network for Autonomous Lockpoints

Manual/Assisted Lock

SW Arch 1
Flowchart: Remote User-Assisted Process

Fully Autonomous Lock

SW Arch 2
High-Level Overview of Autolocking Logic

4. Analysis and Acknowledgement

Our measurements include long-term stability tests and Allan Deviation analysis to ensure the lock remains robust over time.

I am deeply grateful to Asst. Prof. Dr. Santra (Group head, CAQT Lab, IIT Delhi) for his guidance. Special thanks to Deepshikha, Apoorva, and the PhD scholars of CAQT Lab for their consistent technical support throughout this project.