Introducing Origami Cubes: The Future Of Mechanical Computers

A team of eight North Carolina State University researchers have come together to develop cutting-edge technology, pushing the boundaries of what’s possible in computing and memory storage. Their work has culminated in a novel mechanical metastructure that could revolutionize how we store and process information, with their research published seven days ago.

What is a metastructure?

At its core, a metastructure is a highly structured material of smaller units called building blocks. These blocks are designed to exhibit unique properties that traditional materials can’t achieve. In this case, the team has created a metastructure that can reconfigure and reprogram itself, allowing it to store high-density information stably and efficiently.

The Challenge with Traditional Systems

Traditional electronic computing systems rely on silicon chips and binary code (0s and 1s) to process information. However, these systems have limited information density and stability under varying conditions. Mechanical systems, which use physical deformations to encode information, have emerged as a promising alternative. Yet, until now, they have struggled likewise with low information density and the inability to retain stable memory under stress.

The Reprogrammable Mechanical Metastructure

This team’s innovation addresses these challenges head-on. Their metastructure is from rigid cubes connected by elastic hinges, forming flexible and reconfigurable units. These units can transform into various shapes, representing different information bits. By manipulating these shapes, the metastructure can store and process information in a stable and high-density way, helping create a mechanical computer that does not rely on electric components.

How Does It Work?

1. Shape Transformation

The basic unit of this metastructure can change its shape in multiple ways. When stretched or compressed, the structure reconfigures into stable states representing binary information (0s and 1s). Imagine a tiny mechanical block that can flip between two positions — each position denotes a different piece of data.

2. Bistability

A key feature of these units is their bistability, meaning they can snap into two distinct states and remain stable in both without continuous energy input. This is crucial for maintaining memory over time.

3. High Information Density

By combining multiple units into a larger structure, the team has created a system that can store vast amounts of information. Think of it as a puzzle where each piece can change shape and hold data, allowing for a highly compact and efficient memory system.

The above diagram and image demonstrate how the metastructure works.

Practical Applications

1. Information Storage

These metastructures can create robust memory devices less susceptible to damage from external forces, making them ideal for harsh environments.

2. Encryption

The unique configurations of the metastructures can be used for advanced data encryption, providing a new level of security for sensitive information.

3. Mechanical Logic Gates

The structures can function as basic logic gates, the building blocks of computing, paving the way for entirely mechanical computers.

4. Dynamic Displays

Imagine a billboard or a screen that changes its display through mechanical adjustments rather than electronic signals. This could lead to new kinds of interactive and durable displays.

The Future of Computing

This innovation represents a significant leap forward in the field of mechanical computing. By solving the issues of stability and information density, the North Carolina State University team has opened up new possibilities for the future of technology. Mechanical metastructures could complement or even replace electronic systems in certain applications, solving problems with data storage, encryption, and processing. A video of the system can be found at

Works Cited

Li, Yanbin, et al. “Reprogrammable and Reconfigurable Mechanical Computing Metastructures with Stable and High-Density Memory.” Science Advances, vol. 10, no. 26, 26 June 2024,, Accessed 27 June 2024.