Abstract: A neuromorphic system enabling on-chip training includes: synapse arrays where synapse devices are arranged in a cross-bar shape; a final neuron layer including a forward neuron and a backward neuron and connected to an output terminal of a last synapse array; neuron layers including a forward neuron, a backward neuron, and a memory storing signals used during a weighted value update operation of a neural network and arranged between the remaining synapse arrays except for a first and last synapse arrays; and an error calculation circuit detecting and outputting an error value of a target signal and an output signal of the forward neuron of the final neuron layer. Conductances of the synapse devices represent weighted values of the neural network and are changed by the weighted value update operation. Each synapse device is configured with a flash device, and the neuron layers are implemented with ultra-miniature devices.

Abstract: Provided is a 3D capacitor stack and a method for manufacturing the same. The 3D capacitor stack comprises: a drain line electrode having a pillar shape provided in a vertical direction on a substrate surface; a plurality of first insulating layers positioned in first region of an outer circumferential surface of the drain line electrode; a plurality of drains positioned in second regions of an outer circumferential surface of the drain line electrode; a plurality of insulator stacks positioned on side surfaces of the drains; and a plurality of word lines positioned on side surfaces of the insulator stacks.

Abstract: Provided is a 3D synapse device stack, a 3D stackable synapse array using the same, and a method for manufacturing the 3D synapse device stack.

Abstract: Provided is a 3D stackable synapse string, a 3D stackable synapse array using the same, and a method for manufacturing the 3D stackable synapse string.

Abstract: Provided is a 3D synapse device stack, a 3D stackable synapse array using the same, and a method for manufacturing the 3D synapse device stack.

Abstract: A neuromorphic architecture enabling on-chip training includes: synapse arrays where synapse devices are arranged in a cross-bar shape; a final neuron layer including a forward neuron and a backward neuron and connected to an output terminal of a last synapse array; neuron layers including a forward neuron, a backward neuron, and a memory storing signals used during a weighted value update operation of a neural network and arranged between the remaining synapse arrays except for a first and last synapse arrays; and an error calculation circuit detecting and outputting an error value of a target signal and an output signal of the forward neuron of the final neuron layer. Conductances of the synapse devices represent weighted values of the neural network and are changed by the weighted value update operation. Each synapse device is configured with a flash device, and the neuron layers are implemented with ultra-miniature devices.