Abstract:
A logic device is provided which includes an electron monochromator. The electron monochromator includes a quantum dot disposed between first and second tunneling barriers, an emitter coupled to the first tunneling barrier, and a collector coupled to the second tunneling barrier. The logic device also includes a quantum interference device. The quantum interference device includes a source which is coupled to the collector of the electron monochromator.
Abstract:
A field effect transistor includes a body layer comprising a crystalline semiconductor channel region therein, and a gate stack on the channel region. The gate stack includes a crystalline semiconductor gate layer, and a crystalline semiconductor gate dielectric layer between the gate layer and the channel region. Related devices and fabrication methods are also discussed.
Abstract:
A neuromorphic weight cell (NWC) including a resistor ladder including a plurality of resistors connected in series, and a plurality of shunting nonvolatile memory (NVM) elements, each of the shunting NVM elements being coupled in parallel to a corresponding one of the resistors.
Abstract:
A method of storing a sparse weight matrix for a trained artificial neural network in a circuit including a series of clusters. The method includes partitioning the sparse weight matrix into at least one first sub-block and at least one second sub-block. The first sub-block includes only zero-value weights and the second sub-block includes non-zero value weights. The method also includes assigning the non-zero value weights in the at least one second sub-block to at least one cluster of the series of clusters of the circuit. The circuit is configured to perform matrix-vector-multiplication (MVM) between the non-zero value weights of the at least one second sub-block and an input vector during an inference process utilizing the artificial neural network. The sub-blocks containing all zero elements are power gated, thereby reducing overall energy consumption for inference.
Abstract:
A neuromorphic device for the analog computation of a linear combination of input signals, for use, for example, in an artificial neuron. The neuromorphic device provides non-volatile programming of the weights, and fast evaluation and programming, and is suitable for fabrication at high density as part of a plurality of neuromorphic devices. The neuromorphic device is implemented as a vertical stack of flash-like cells with a common control gate contact and individually contacted source-drain (SD) regions. The vertical stacking of the cells enables efficient use of layout resources.
Abstract:
A method of manufacturing a field effect transistor includes forming a fin on a substrate, forming source and drain electrodes on opposite sides of the fin, forming a gate stack on a channel portion of the fin between the source and drain electrodes, forming gate spacers on extension portions of the fin on opposite sides of the gate stack, removing at least portions of the gate spacers to expose the extension portions of the fin, and hydrogen annealing the extension portions of the fin. Following the hydrogen annealing of the extension portions of the fin, the channel portion of the fin has a first width and the extension portions of the fin have a second width greater than the first width.
Abstract:
A neuromorphic device for the analog computation of a linear combination of input signals, for use, for example, in an artificial neuron. The neuromorphic device provides non-volatile programming of the weights, and fast evaluation and programming, and is suitable for fabrication at high density as part of a plurality of neuromorphic devices. The neuromorphic device is implemented as a vertical stack of flash-like cells with a common control gate contact and individually contacted source-drain (SD) regions. The vertical stacking of the cells enables efficient use of layout resources.
Abstract:
A semiconductor device includes a series of metal routing layers and a complementary pair of planar field-effect transistors (FETs) on an upper metal routing layer of the metal routing layers. The upper metal routing layer is M3 or higher. Each of the FETs includes a channel region of a crystalline material. The crystalline material may include polycrystalline silicon. The upper metal routing layer M3 or higher may include cobalt.
Abstract:
A method of storing a sparse weight matrix for a trained artificial neural network in a circuit including a series of clusters. The method includes partitioning the sparse weight matrix into at least one first sub-block and at least one second sub-block. The first sub-block includes only zero-value weights and the second sub-block includes non-zero value weights. The method also includes assigning the non-zero value weights in the at least one second sub-block to at least one cluster of the series of clusters of the circuit. The circuit is configured to perform matrix-vector-multiplication (MVM) between the non-zero value weights of the at least one second sub-block and an input vector during an inference process utilizing the artificial neural network. The sub-blocks containing all zero elements are power gated, thereby reducing overall energy consumption for inference.
Abstract:
A field effect transistor (FET) for an nFET and/or a pFET device including a substrate and a fin including at least one channel region decoupled from the substrate. The FET also includes a source electrode and a drain electrode on opposite sides of the fin, and a gate stack extending along a pair of sidewalls of the channel region of the fin. The gate stack includes a gate dielectric layer and a metal layer on the gate dielectric layer. The FET also includes an oxide separation region separating the channel region of the fin from the substrate. The oxide separation region includes a dielectric material that includes a portion of the gate dielectric layer of the gate stack. The oxide separation region extends completely from a surface of the channel region facing the substrate to a surface of the substrate facing the channel region.