Embodiments provide a selector device for selecting a memory cell. The selector device includes a first electrode; a second electrode; and a switching layer sandwiched between the first electrode and the second electrode. The switching layer includes at least one metal rich layer and at least one chalcogenide rich layer. The metal rich layer includes at least one of a metal or a metal compound, wherein metal content of the metal rich layer is greater than 50 at. %. The chalcogenide content of the chalcogenide rich layer is greater than 50 at. %.

An apparatus includes an in-memory compute circuit that includes a memory circuit configured to generate a set of products by combining received input values with respective weight values stored in rows of the memory circuit, and to combine the set of products to generate an accumulated output value. The in-memory compute circuit may further include a control circuit and a plurality of routing circuits, including a first routing circuit coupled to a first set of rows of the memory circuit. The control circuit may be configured to cause the first routing circuit to route groups of input values to different ones of the first set of rows over a plurality of clock cycles, and the memory circuit to generate, on a clock cycle following the plurality of clock cycles, a particular accumulated output value that is computed based on the routed groups of input values.

A neuromorphic device includes a plurality of cell tiles including a cell array including a plurality of memory cells storing a weight of a neural network, a row driver connected to the plurality of memory cells, and cell analog-digital converters connected to the plurality of memory cells and converting cell currents into a plurality of pieces of digital cell data, a reference tile including a plurality of reference cells, a reference row driver connected to the plurality of reference cells, and reference analog-digital converters connected to the plurality of reference cells and converting reference currents read via the plurality of reference column lines into a plurality of pieces of digital reference data, and a comparator circuit configured to compare the plurality of pieces of digital cell data with the plurality of pieces of digital reference data, respectively.

A resistive processing unit cell includes a weight storage device to store a weight value of the resistive processing unit cell, and multiple circuit blocks. Each circuit block includes a weight update circuit coupled to dedicated update control lines, and a weight read circuit coupled to dedicated read control lines. The circuit blocks are configured to operate in parallel to (i) perform separate weight read operations in which each read circuit generates a read current based on a stored weight value, and outputs the read current on the dedicated read control lines of the read circuit, and (ii) perform separate weight update operations in which each update circuit receives respective update control signals on the dedicated update control lines, generates update currents based on the respective update control signals, and applies the update current to the weight storage device to adjust the weight value based on the update current.

A system includes a processor, and a resistive processing resistive processing unit coupled to the processor. The resistive processing unit includes an array of cells, wherein the cells respectively include resistive devices, wherein at least a portion of the resistive devices are programmable to store weight values of a given matrix in the array of cells. When the given matrix is stored in the array of cells, the processor is configured to perform a weight extraction process. The weight extraction process applies a set of input vectors to the resistive processing unit to perform analog matrix-vector multiplication operations on the stored matrix, obtains a set of output vectors resulting from the analog matrix-vector multiplication operations, and determines weight values of the given matrix stored in the array of cells utilizing the set of input vectors and the set of output vectors.

A DIMA semiconductor structure is disclosed. The DIMA semiconductor structure includes a frontend including a semiconductor substrate, a transistor switch of a memory cell coupled to the semiconductor substrate and a computation circuit on the periphery of the frontend coupled to the semiconductor substrate. Additionally, the DIMA includes a backend that includes an RRAM component of the memory cell that is coupled to the transistor switch.

Numerous examples of a precision programming apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. In one example, a neuron output circuit for providing a current to program as a weight value in a selected memory cell in a vector-by-matrix multiplication array is disclosed, the neuron output circuit comprising a first adjustable current source to generate a scaled current in response to a neuron current to implement a positive weight, and a second adjustable current source to generate a scaled current in response to a neuron current to implement a negative weight.

An apparatus includes an in-memory compute circuit that includes a memory circuit configured to generate a set of products by combining received input values with respective weight values stored in rows of the memory circuit, and to combine the set of products to generate an accumulated output value. The in-memory compute circuit may further include a control circuit and a plurality of routing circuits, including a first routing circuit coupled to a first set of rows of the memory circuit. The control circuit may be configured to cause the first routing circuit to route groups of input values to different ones of the first set of rows over a plurality of clock cycles, and the memory circuit to generate, on a clock cycle following the plurality of clock cycles, a particular accumulated output value that is computed based on the routed groups of input values.

Technology is disclosed herein for per pin internal reference voltage generation for data receivers in non-volatile memory systems. A receiving circuit may have an on-die voltage generator that has inputs to receive a separate voltage magnitude select signal for each data receiver on the receiving circuit. The on-die voltage generator provides a separate reference voltage for each data receiver. This allows the reference voltage for each data receiver to be calibrated separately. A separate reference voltage for each data receiver compensates for variations between data paths, and provides for a wider data valid window than if the same reference voltage were used for all data receivers. Generating the different reference voltages on-die can potentially require a large area, as well as consume considerable power and/or current. A voltage divider and multiplexers may provide the different reference voltages, which saves space and is power and current efficient.

The present invention discloses a computing array based on 1T1R device, operation circuits and operating methods thereof. The computing array has 1T1R arrays and a peripheral circuit; the 1T1R array is configured to achieve operation and storage of an operation result, and the peripheral circuit is configured to transmit data and control signals to control operation and storage processes of the 1T1R arrays; the operation circuits are respectively configured to implement a 1-bit full adder, a multi-bit step-by-step carry adder and optimization design thereof, a 2-bit data selector, a multi-bit carry select adder and a multi-bit pre-calculation adder; and in the operating method corresponding to the operation circuit, initialized resistance states of the 1T1R devices, word line input signals, bit line input signals and source line input signals are controlled to complete corresponding operation and storage processes.