Abstract: A method and system for training a neural network are described. The method includes providing at least one continuously differentiable model of the neural network. The at least one continuously differentiable model is specific to hardware of the neural network. The method also includes iteratively training the neural network using the at least one continuously differentiable model to provide at least one output for the neural network. Each iteration uses at least one output of a previous iteration and a current continuously differentiable model of the at least one continuously differentiable model.

Abstract: A computing cell and method for performing a digital XNOR of an input signal and weights are described. The computing cell includes at least one pair of FE-FETs and a plurality of selection transistors. The pair(s) of FE-FETs are coupled with a plurality of input lines and store the weight. Each pair of FE-FETs includes a first FE-FET that receives the input signal and stores a first weight and a second FE-FET that receives the input signal complement and stores a second weight. The selection transistors are coupled with the pair of FE-FETs.

Abstract: A semiconductor memory device and method for providing the semiconductor memory device are described. The semiconductor memory device includes a ferroelectric capacitor. The ferroelectric capacitor includes a first electrode, a second electrode and a multilayer insulator structure between the first and second electrodes. The multilayer insulator structure includes at least one ferroelectric layer and at least one dielectric layer. The at least one ferroelectric layer and the at least one dielectric layer share at least one interface and have a strong polarization coupling.

Abstract: A memory cell and method for utilizing the memory cell are described. The memory cell includes at least one ferroelectric transistor (FE-transistor) and at least one selection transistor coupled with the FE-transistor. An FE-transistor includes a transistor and a ferroelectric capacitor for storing data. The ferroelectric capacitor includes ferroelectric material(s). In some aspects, the memory cell consists of a FE-transistor and a selection transistor. In some aspects, the transistor of the FE-transistor includes a source, a drain and a gate coupled with the ferroelectric capacitor. In this aspect, the selection transistor includes a selection transistor source, a selection transistor drain and a selection transistor gate. In this aspect, a write port of the memory cell is the selection transistor source or the selection transistor drain. The other of the selection transistor source and drain is coupled to the ferroelectric capacitor.

Abstract: A semiconductor device and method for providing a semiconductor device are described. The semiconductor device includes a channel, a gate, and a multilayer gate insulator structure between the gate and the channel. The multilayer gate insulator structure includes at least one ferroelectric layer and at least one dielectric layer. The at least one ferroelectric layer and the at least one dielectric layer share at least one interface and have a strong polarization coupling.

Abstract: A computing cell and method for performing a digital XNOR of an input signal and weights are described. The computing cell includes at least one pair of FE-FETs and a plurality of selection transistors. The pair(s) of FE-FETs are coupled with a plurality of input lines and store the weight. Each pair of FE-FETs includes a first FE-FET that receives the input signal and stores a first weight and a second FE-FET that receives the input signal complement and stores a second weight. The selection transistors are coupled with the pair of FE-FETs.

Abstract: A weight cell including first and second bi-directional memory elements each configured to switch between a first resistance state and a second resistance state different than the first resistance state. A first input line is connected to a first terminal of the first bi-directional memory element, and a second input line is connected to the first terminal of the second bi-directional memory element. A first diode in forward bias connects the second terminal of the first bi-directional memory element to a first output line, a second diode in reverse bias connects the second terminal of the second bi-directional memory element to a second output line, a third diode in reverse bias connects the second terminal of the first bi-directional memory element to the second output line, and a fourth diode in forward bias connects the second terminal of the second bi-directional memory element to the first output line.

Abstract: A hardware cell and method for performing a digital XNOR of an input signal and weights are described. The hardware cell includes input lines, a plurality of pairs of magnetic junctions, output transistors and at least one selection transistor coupled with the output transistors. The input lines receive the input signal and its complement. The magnetic junctions store the weight. Each magnetic junction includes a reference layer, a free layer and a nonmagnetic spacer layer between the reference layer and the free layer. The free layer has stable magnetic states and is programmable using spin-transfer torque and/or spin-orbit interaction torque. The first magnetic junction of a pair receives the input signal. The second magnetic junction of the pair receives the input signal complement. The output transistors are coupled with the magnetic junctions such that each pair of magnetic junctions forms a voltage divider. The output transistors form a sense amplifier.

Abstract: A hardware device and method for performing a multiply-accumulate operation are described. The device includes inputs lines, weight cells and output lines. The input lines receive input signals, each of which is has a magnitude and a phase and can represent a complex value. The weight cells couple the input lines with the output lines. Each of the weight cells has an electrical admittance corresponding to a weight. The electrical admittance is programmable and capable of being complex valued. The input lines, the weight cells and the output lines form a crossbar array. Each of the output lines provides an output signal. The output signal for an output line is a sum of an input signal for each of the input lines connected to the output line multiplied by the electrical admittance of each of the weight cells connecting the input lines to the output line.

Abstract: A memory device and method for providing the memory device are described. The memory device includes word lines, a first plurality of bit lines, a second plurality of bit lines and selectorless memory cells. Each selectorless memory cell is coupled with a word line, a first bit line of the first plurality of bit lines and a second bit line of the second plurality of bit lines. The selectorless memory cell includes first and second magnetic junctions. The first and second magnetic junctions are each programmable using a spin-orbit interaction torque. The word line is coupled between the first and second magnetic junctions. The first and second bit lines are coupled with the first and second magnetic junctions, respectively. The selectorless memory cell is selected for a write operation based on voltages in the word line, the first bit line and the second bit line.

Abstract: A neuromorphic architecture for providing variable precision in a neural network, through programming. Logical pre-synaptic neurons are formed as configurable sets of physical pre-synaptic artificial neurons, logical post-synaptic neurons are formed as configurable sets of physical post-synaptic artificial neurons, and the logical pre-synaptic neurons are connected to the logical post-synaptic neurons by logical synapses each including a set of physical artificial synapses. The precision of the weights of the logical synapses may be varied by varying the number of physical pre-synaptic artificial neurons in each of the logical pre-synaptic neurons, and/or by varying the number of physical post-synaptic artificial neurons in each of the logical post-synaptic neurons.

Abstract: A neuromorphic multi-bit digital weight cell configured to store a series of potential weights for a neuron in an artificial neural network. The neuromorphic multi-bit digital weight cell includes a parallel cell including a series of passive resistors in parallel and a series of gating transistors. Each gating transistor of the series of gating transistors is in series with one passive resistor of the series of passive resistors. The neuromorphic cell also includes a series of programming input lines connected to the series of gating transistors, an input terminal connected to the parallel cell, and an output terminal connected to the parallel cell.

Abstract: A semiconductor device including: a substrate; a first active layer on the substrate and including a first channel between a source and a drain; a second active layer stacked on the first active layer, the second active layer including a second channel between the source and the drain; a first gate corresponding to the first channel; and a second gate electrically separated from the first gate and corresponding to the second channel.

Abstract: A system of unipolar digital logic. Ferroelectric field effect transistors having channels of a first polarity, are combined, in circuits, with simple field effect transistors having channels of the same polarity, to form logic gates and/or memory cells.

Abstract: A finFET device can include a source/drain contact recess having an optimal depth beyond which an incremental decrease in a spreading resistance value for a horizontal portion of a source/drain contact in the recess provided by increased depth may be less than an incremental increase in total resistance due to the increase in the vertical portion of the source/drain contact at the increased depth.

Abstract: A system of unipolar digital logic. Ferroelectric field effect transistors having channels of a first polarity, are combined, in circuits, with simple field effect transistors having channels of the same polarity, to form logic gates and/or memory cells.

Abstract: Integrated circuit devices may include a stack that includes channel regions and gate electrodes stacked in an alternating sequence in a vertical direction. The channel regions may include impurities having a first conductivity type. The integrated circuit devices may also include source/drain regions on respective opposing sides of the stack, and the source/drain regions may be spaced apart from each other in a horizontal direction and may include impurities having a second conductivity type that is different from the first conductivity type. The integrated circuit devices may further include extension regions that may be between respective ones of channel regions and one of the source/drain regions and may include impurities having the second conductivity type. Each of the extension regions may have a thickness in the vertical direction that is less than those of the channel regions and the one of the source/drain regions.

Abstract: Exemplary embodiments provide for fabricating a biaxially strained nanosheet.

Abstract: A semiconductor device including: a substrate; a first active layer on the substrate and including a first channel between a source and a drain; a second active layer stacked on the first active layer, the second active layer including a second channel between the source and the drain; a first gate corresponding to the first channel; and a second gate electrically separated from the first gate and corresponding to the second channel.

Abstract: A bidirectional memory cell includes a write unit and a read unit. The write unit and the read unit each include an MTJ structure having a first and second pinned layers and a free layer. The first and second pinned layers are separated from the free layer by at least one tunnel barrier. The first pinned layer is electrically coupled to a first write line through a first diode. The second pinned layer is electrically connected to a second word line through a second diode. The free layer is electrically coupled to a first bit line. Additionally, the free layer of the read unit is magnetically coupled to the free layer of the write unit.