Leaky FinFET for Reservoir Computing with Temporal Signal Processing
Joon‐Kyu Han, Seong‐Yun Yun, Ji‐Man Yu, Yang‐Kyu Choi
Abstract
Reservoir computing can greatly reduce the hardware and training costs of recurrent neural networks with temporal data processing. To implement reservoir computing in a hardware form, physical reservoirs transforming sequential inputs into a high-dimensional feature space are necessary. In this work, a physical reservoir with a leaky fin-shaped field-effect transistor (L-FinFET) is demonstrated by the positive use of a short-term memory property arising from the absence of an energy barrier to suppress the tunneling current. Nevertheless, the L-FinFET reservoir does not lose its multiple memory states. The L-FinFET reservoir consumes very low power when encoding temporal inputs because the gate serves as an enabler of the write operation, even in the off-state, due to its physical insulation from the channel. In addition, the small footprint area arising from the scalability of the FinFET due to its multiple-gate structure is advantageous for reducing the chip size. After the experimental proof of 4-bit reservoir operations with 16 states for temporal signal processing, handwritten digits in the Modified National Institute of Standards and Technology dataset are classified by reservoir computing.