Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device - Takumi Kotooka; Sam Lilak; Adam Z. Stieg; James K. Gimzewski; Naoyuki Sugiyama; Yuichiro Tanaka; Takuya Kawabata; Ahmet Karacali; Hakaru Tamukoh; Yuki Usami; Hirofumi Tanaka (Advanced Electronic Materials, 2024)
Abstract The artificial intelligence (AI) paradigm shifts from software to implementing general‐purpose or application‐specific hardware systems with lower power requirements. This study explored a material physical reservoir consisting of a material random network, called in‐materio physical reservoir computing (RC) to achieve efficient hardware systems. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions as reservoir nodes, demonstrated the requisite characteristics of an in‐materio physical reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. The power consumption of the in‐materio physical reservoir is 0.07 nW per nanojunctions, confirming its highly efficient information processing system. As a hardware reservoir, the devices successfully performed waveform generation tasks. Finally, a voice classification by an in‐materio physical reservoir is achieved over 80%, comparable to an RC software simulation. In‐materio physical RC with rich nonlinear dynamics has huge potential for next‐generation hardware‐based AI.