Provided are a weighting device that may be driven at a low voltage and is capable of embodying multi-level weights, a neural network, and a method of operating the weighting device. The weighting device includes a switching layer that may switch between a high resistance state and a low resistance state based on a voltage applied thereto and a charge trap material layer that traps or discharges charges according to a resistance state of the switching layer. The weighting device may be used for controlling a weight in a neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A weighting device comprising: a substrate; a first transistor including, a source region and a shared region spaced apart from each other on the substrate; a source electrode electrically connected to the source region; a first channel region between the source region and the shared region; a first gate insulation layer arranged on the first channel region; a charge trap material layer arranged on the first gate insulation layer; a switching layer arranged on the charge trap material layer and configured to switch between a low resistance state and a high resistance state; and a first gate electrode arranged on the switching layer; and a second transistor including, a drain region spaced apart from the shared region on the substrate; a drain electrode electrically connected to the drain region; a second channel region between the shared region and the drain region; a second gate insulation layer arranged on the second channel region; and a second gate electrode arranged on the second gate insulation layer, wherein the charge trap material layer faces a portion of the shared region across the first gate insulation layer. 2. The weighting device of claim 1 wherein, the first transistor further includes an anti-leakage region on the substrate, the anti-leakage region constitutes a PN diode structure together with the source region, and the source electrode contacts the anti-leakage region. 3. The weighting device of claim 2, wherein, except a surface of the anti-leakage region contacting the source electrode, the anti-leakage region is surrounded by the source region. 4. The weighting device of claim 1, wherein the first channel region and the second channel region have conductive types opposite each other. 5. The weighting device of claim 4 wherein, the first transistor further includes a well, the well surrounds the source region, the first channel region, and the shared region at the substrate, and the well has a conductive type opposite that of the second channel region. 6. The weighting device of claim 1, wherein the first gate electrode and the source electrode are electrically connected to each other. 7. The weighting device of claim 1, further comprising: a boosting electrode that faces the charge trap material layer across the first gate insulation layer and the substrate. 8. The weighting device of claim 1, wherein the switching layer comprises: a nano-filamentary material switchable to the low resistance state as conductive nano-filaments are formed therein when a voltage is applied to the switching layer. 9. The weighting device of claim 1, wherein the switching layer comprises: a PN diode layer. 10. A method of operating the weighting device of claim 1, the method comprising: opening the second channel region by applying a selection voltage to the second gate electrode; applying a write voltage to the drain electrode; and grounding the first gate electrode, wherein data is written to the charge trap material layer as charges are introduced into the charge trap material layer via the first gate electrode due to the write voltage. 11. A method of operating the weighting device of claim 1, the method comprising: opening the second channel region by applying a selection voltage to the second gate electrode; applying an erase voltage to the first gate electrode; and grounding the drain electrode, wherein data is erased as charges trapped in the charge trap material layer are removed by the erase voltage. 12. A method of operating the weighting device of claim 1, the method comprising: opening the second channel region by applying a selection voltage to the second gate electrode; applying a read voltage to the source electrode; and reading an amount of charges trapped in the charge trap material layer by measuring a sensing current at the drain electrode. 13. The method of claim 10 wherein, the applying of the write voltage includes adjusting a weight by applying a pulse voltage to the drain electrode, and the adjusting of the weight is controlled based on a number of times of applying the pulse voltage. 14. The method of claim 11 wherein, the applying of the erase voltage includes adjusting a weight by applying a pulse voltage to the first gate electrode, and the adjusting of the weight is controlled based on a number of times of applying the pulse voltage. 15. A neural network comprising: a plurality of weighting devices of claim 1; and a plurality of neuron transistors configured to selectively switch gate voltages applied to the plurality of weighting devices. 16. The neural network of claim 15 wherein, the plurality of neuron transistors are 2-dimensionally arranged, drain electrodes of the plurality of neuron transistors are respectively connected to first gate electrodes of the plurality of weighting devices, and the neural network further comprises: a select line connected to gate electrodes of neuron transistors arranged at a same column, the select line being configured to apply a selection voltage; and an input line connected to source electrodes of neuron transistors arranged at a same row, the input line being configured to apply an input voltage. 17. The neural network of claim 15, wherein the plurality of weighting devices are 1-dimensionally or 2-dimensionally arranged. 18. A method of operating the neural network of claim 15 for supervised learning, wherein input information to be input via the plurality of neuron transistors and output information to be output via the plurality of weighting devices is determined in advance and a weight of the neural network corresponding thereto is determined. 19. A method of operating the neural network of claim 15 for unsupervised learning, wherein a weight of the neural network is autonomously determined based on input information input via neuron transistors. 20. A weighting device comprising: a first transistor including a third gate electrode, a first electrode, and a second electrode; a second transistor sharing the second electrode with the first transistor and including a fourth gate electrode and a third electrode; a third transistor including a fifth gate electrode and a fourth electrode and configured to use a shared region as a drain; a fourth transistor including a sixth gate electrode and a fifth electrode and configured to use the shared region as a source; a switching layer arranged on the first electrode and the third electrode and configured to switch between a high resistance state and a low resistance state; a charge trap material layer arranged on the switching layer and configured to trap or release charges according to a resistance state of the switching layer; and an interconnect interconnecting the charge trap material layer and the sixth gate electrode.