# Non‐Volatile Phase Modulation with Ultralow Energy Consumption Enabled by 2D Ferroelectric/TMD Heterostructures

## TL;DR

Researchers developed a low-energy, non-volatile optical phase modulator using a 2D ferroelectric material and a heterostructure design for photonic in-memory computing.

## Contribution

A compact, low-loss, and ultra-low energy phase modulator using 2D ferroelectric/TMD heterostructures for photonic in-memory computing.

## Key findings

- The device achieves ultra-low switching energy of 2.5 pJ per cycle and fast write speed of 5 V/µs.
- It demonstrates stable 8-bit memory with projected retention beyond 10 years.
- The modulator achieved 92% accuracy on the MNIST handwritten digit recognition task in an optical neural network.

## Abstract

Achieving non‐volatile, low‐loss phase modulation with ultra‐low energy consumption remains a challenge in photonic in‐memory computing. Inspired by electrical memory technologies, mechanisms such as ion‐migration, phase change transitions, and ferroelectric polarization have been explored in photonic platforms for memory functions. However, existing materials typically require large device footprints to achieve effective optical index tuning, leading to increased insertion loss and energy consumption. Here, we demonstrate a compact non‐volatile phase modulator by incorporating 2D ferroelectric CuInP2S6 (CIPS) into a WS2/CIPS/graphene heterostructure, integrated on a SiN microring resonator. This vertical configuration leverages Cu+‐induced polarization in CIPS to electrostatically tune the refractive index of WS2 without introducing additional optical loss or static power consumption. The intralayer Cu+‐mediated ferroelectric switching (free from domain wall motion) and high dielectric constant enable the device to operate with an ultra‐low switching energy of 2.5 pJ per cycle, a fast write speed of 5 V/µs, and an insertion loss of 0.2 dB. The device further shows stable multi‐level (8‐bit) memory, with projected retention beyond 10 years. We showcase its potential in photonic in‐memory computing by implementing the modulator within an optical neural network, achieving 92% accuracy on the MNIST handwritten digit recognition, establishing new avenues for hardware‐accelerated neural networks.

We demonstrate a hybrid WS2/CuInP2S6/graphene heterostructure integrated on a silicon nitride microring resonator for non‐volatile optical phase modulation with ultra‐low energy consumption and low insertion loss. While CIPS alone does not provide efficient optical index modulation, the engineered proposed device structure converts ferroelctric domain switching into robust, low‐loss, and non‐volatile phase tunning.