Abstract:
The present invention provides a shock recording device, comprising: a vibration energy harvester comprising a first electrode and a second electrode, the vibration energy harvester converting an energy of a shock applied thereto into a potential difference between the first electrode and the second electrode; and a ferroelectric transistor comprising a gate electrode, a source electrode, and a drain electrode, the ferroelectric transistor further comprising a stacked structure of a ferroelectric layer and a semiconductor layer. The gate electrode is electrically connected to the first electrode. The source electrode is electrically connected to the second electrode. This shock recording device does not need a power source used to record a shock.
Abstract:
The present invention provides a shock recording device consisting of: an electric power source; a vibration energy harvester comprising a first electrode and a second electrode, the vibration energy harvester converting an energy of a shock applied thereto into a potential difference between the first electrode and the second electrode; a first transistor comprising a first gate electrode, a first source electrode, and a first drain electrode, the first transistor further comprising a stacked structure of a ferroelectric layer and a semiconductor layer; and a second transistor comprising a second gate electrode, a second source electrode, and a second drain electrode. The second gate electrode is electrically connected to the first electrode. The second drain electrode is electrically connected to the electric power source. The second source electrode is electrically connected to the first gate electrode. The first source electrode is electrically connected to the second electrode.
Abstract:
A neural network circuit includes an error calculating circuit that generates an error voltage signal having a magnitude in accordance with a time difference between an output signal and a teaching signal corresponding to the output signal. A weight change pulse voltage signal is input to a synapse circuit of a neural network circuit element including a neuron circuit that output the weight change pulse voltage signal, and a switching pulse voltage signal is input to a synapse circuit of a neural network circuit element other than the neural network circuit element including the neuron circuit that output the switching pulse voltage signal. The neural network circuit element changes the amplitude of the weight change pulse voltage signal on the basis of the error voltage signal generated by the error calculating circuit.
Abstract:
A random number generating device of the present disclosure includes: an arithmetic random number generator that generates an arithmetic random number sequence; an arithmetic random number converter that sequentially reads at least one arithmetic random number from the arithmetic random number sequence and converts a value of the read arithmetic random number into a voltage or current value of at least two predetermined levels of gray scale having an identical polarity; a hysteresis unit that outputs values depending on a presently-input voltage or current value and a previously-input voltage or current value with respect to the sequentially-input voltage or current value; and a threshold processor that binarizes the output of the hysteresis unit.