Mechanical transistors for logic-with-memory computing
Huyue Chen, Chao Song, Jiahao Wu, Bihui Zou, Zhihan Zhang, An Zou,, Yuljae Cho, Zhaoguang Wang, Wenming Zhang, Lei Shao, Jaehyung Ju

TL;DR
This paper introduces a mechanical transistor that enables logic-with-memory computing using thermal and nonlinear soft actuators, paving the way for non-electronic, reprogrammable mechanical computing systems.
Contribution
It presents the design of a mechanical transistor capable of forming complex logic and memory circuits, advancing the development of universal mechanical computing architectures.
Findings
Successfully demonstrated mechanical logic gates and memory units
Fewer units needed compared to electronic counterparts
Established a reprogrammable mechanical processing core
Abstract
As a potential revolutionary topic in future information processing, mechanical computing has gained tremendous attention for replacing or supplementing conventional electronics vulnerable to power outages, security attacks, and harsh environments. Despite its potential for constructing intelligent matter towards nonclassical computing systems beyond the von Neumann architecture, most works on mechanical computing demonstrated that the ad hoc design of simple logic gates cannot fully realize a universal mechanical processing framework involving interconnected arithmetic logic components and memory. However, such a logic-with-memory computing architecture is critical for complex and persistent state-dependent computations such as sequential logic. Here we propose a mechanical transistor (M-Transistor), abstracting omnipresent temperatures as the input-output mechanical bits, which…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Neural Networks and Applications
