Stochastic Data Processing Based on Single Electrons Using Nano Field-Effect Transistors


Katsuhiko Nishiguchi, Yukinori Ono, and Akira Fujiwara
Physical Science Laboratory

 Although the miniaturization of Si metal-oxide-semiconductor field-effect transistors (MOSFETs) continues to improve the performance of various consumer electronics, the need to guarantee error-free operation of MOSFETs makes it difficult to reduce supply voltage, which leads to ever increasing current density and power consumption. In this work, conversely, we reduce the current density of nanoscale FETs ultimately and then utilize its shot noise to realize a stochastic data processing for pattern recognition with high flexibility [1].
 The device is composed of two transistors fabricated on a silicon-on-insulator wafer (Fig. 1). The first transistor (T-FET) has a two-layer gate: an upper gate (UG) is used to induce an inversion layer and a lower gate (LG) forms an energy barrier in the undoped channel of T-FET. As a result, an electron-storage node (MN) electrically isolated from an electron source (ES) is formed. The two layer gate can eliminate undesired leakage current originating from p-n junctions, which is a well-known issue in conventional FETs [1, 2]. Therefore, highly controllable single-electron transfer from the ES to the MN can be achieved using the LG. The single electrons transferred to the MN are detected by the other transistor (D-FET) as shown in Fig. 2. The optimization of the device structure and operation conditions allows single-electron detection even at room temperature [3]. As analysis of the time interval δt of each electron transfer to the MN revealed that the single-electron transfer is based on a Poissonian process (inset of Fig. 2), which corresponds to the real-time monitoring of shot noise in FETs with single-electron resolution.
 The Poissonian stochastic behavior of such single-electron transfer can be applied to a stochastic data-processing circuit for image-pattern recognition. The circuit recognizes the input pattern as one of the reference patterns with probability correlating to the similarity between the reference and input patterns as shown in Fig. 3. Such probability can be electrically controlled by the LG. These features allow flexible pattern recognition especially for corrupted input patterns, which promises to apply high time and power efficiency to the circuit like in the human brain.

[1] K. Nishiguchi et al., Appl. Phys. Lett. 92 (2008) 062105.
[2] K. Nishiguchi et al., IEEE Electron Device Lett. 28 (2007) 48.
[3] K. Nishiguchi et al., Jpn. J. Appl. Phys. 47 (2008) 8305.

Fig. 1. Device structure based on Si transistors.
(a) Schematic view. (b) Scanning electron microscope.
Fig. 2. Real-time monitoring of single-electron transfer. Inset: Histogram of the time interval between each single-electron transfer to the MN.
Fig. 3. Stochastic pattern recognition. The reference pattern most similar to the input pattern is selected.

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