Different from digital computers, mechanical computers use mechanical components for computation and information storage. The challenges involved in mechanical computing include the need for stable memory and high-density information storage.
Researchers at North Carolina State University have designed a mechanical computer inspired by kirigami. The complex system utilizes interconnected polymer cubes to store, retrieve, and erase data without the need for electronic components. Additionally, the system features a reversible function that provides users with control over data editing permissions and the locking of data in place.
“We were interested in doing a couple of things here,” says Jie Yin, co-corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at NC State. “First, we were interested in developing a stable, mechanical system for storing data.
“Second, this proof-of-concept work focused on binary computing functions with a cube being either pushed up or pushed down – it’s either a 1 or a 0. But we think there is potential here for more complex computing, with data being conveyed by how high a given cube has been pushed up. We’ve shown within this proof-of-concept system that cubes can have five or more different states. Theoretically, that means a given cube can convey not only a 1 or a 0 but also a 2, 3, or 4.”
The new mechanical computer is built using 1-centimeter plastic cubes as the fundamental units, which are organized into functional units comprising 64 interconnected cubes. These units draw inspiration from kirigami, the art of cutting and folding paper. Yin and his colleagues have utilized kirigami principles to create three-dimensional materials that are sliced into interconnected cubes.
Manipulating any of the cubes by pushing them up or down alters the geometry or architecture of all the connected cubes. This adjustment can be achieved by physically pushing one of the cubes or by affixing a magnetic plate to the top of the functional unit and using a magnetic field to push it up or down remotely. The functional units, which consist of 64 cubes, can be combined into more intricate metastructures to increase data storage capacity and enable more complex computations.
Elastic tape connects the cubes, and altering the configuration of functional units is necessary to modify data. This involves users pulling on the metastructure’s edges to stretch the elastic tape, allowing them to reposition the cubes. Upon releasing the metastructure, the tape contracts, securely locking the cubes and the data in place.
“One potential application for this is that it allows for users to create three-dimensional, mechanical encryption or decryption,” says Yanbin Li, first author of the paper and a postdoctoral researcher at NC State. “For example, a specific configuration of functional units could serve as a 3D password.
“And the information density is quite good,” Li says. “Using a binary framework – where cubes are either up or down – a simple metastructure of 9 functional units has more than 362,000 possible configurations.”
“But we’re not necessarily limited to a binary context,” says Yin. “Each functional unit of 64 cubes can be configured into a wide variety of architectures, with cubes stacked up to five cubes high. This allows for the development of computing that goes well beyond binary code. Our proof-of-concept work here demonstrates the potential range of these architectures, but we have not developed code that capitalizes on those architectures. We’d be interested in collaborating with other researchers to explore the coding potential of these megastructures.”
“We’re also interested in exploring the potential utility of these megastructures to create haptic systems that display information in a three-dimensional context rather than as pixels on a screen,” says Li.
Journal reference:
- Yanbin Li, Shuangyue Yu, Haitao Qing, Yaoye Hong, Yao Zhao, Fangjie Qi, Hao Su and Jie Yin. Reprogrammable and Reconfigurable Mechanical Computing Metastructures with Stable and High-Density Memory. Science Advances, 2024; DOI: 10.1126/sciadv.adk7220