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CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm²switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

A major challenge for graphene applications is the lack of mass production technology for large‐scale and high‐quality graphene growth and transfer. Here, a roll‐to‐roll (R2R) dry transfer process for large‐scale graphene grown by chemical vapor deposition is reported. The process is fast, controllable, and environmentally benign. It avoids chemical contamination and allows the reuse of graphene growth substrates. By controlling tension and speed of the R2R dry transfer process, the electrical sheet resistance is achieved as 9.5 kΩ sq⁻¹, the lowest ever reported among R2R dry transferred graphene samples. The R2R dry transferred samples are used to fabricate graphene‐based field‐effect transistors (GFETs) on polymer. It is demonstrated that these flexible GFETs feature a near‐zero doping level and a gate leakage current one to two orders of magnitude lower than those fabricated using wet‐chemical etched graphene samples. The scalability and uniformity of the R2R dry transferred graphene is further demonstrated by successfully transferring a 3 × 3 in²sample and measuring its field‐effect mobility with 36 millimeter‐scaled GFETs evenly spaced on the sample. The field‐effect mobility of the R2R dry transferred graphene is determined to be 205 ± 36 cm² V⁻¹.