Abstract: Numerous examples are disclosed of an artificial neural network that comprises vector-by-matrix multiplication arrays utilizing analog outputs. In one example, a system comprises a vector by matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns; and an output circuit to receive a respective neuron current from respective columns of the vector by matrix multiplication array and to generate a respective output voltage, the output circuit comprising a neuron scalar to generate a scaled current from the received respective neuron current, and a current-to-voltage converter to convert the scaled current into the respective output voltage.

Abstract: In one example, a method comprises programming a plurality of analog neural non-volatile memory cells in an array of analog neural non-volatile memory cells to store one of N different values, where Nis a number of different levels that can be stored in any of the analog neural non-volatile memory cells; measuring a current drawn by the plurality of analog neural non-volatile memory cells; comparing the measured current to a target value; and identifying the plurality of the analog neural non-volatile memory cells as bad if the difference between the measured value and the target value exceeds a threshold.

Abstract: Examples of programming circuits and methods are provided. In one example, an adjustable programming circuit comprises a first adjustable voltage divider; a second adjustable voltage divider; a first operational amplifier, wherein an output terminal of the first operational amplifier provides a first programming voltage; and a second operational amplifier, wherein the first input terminal of the second operational amplifier is coupled to the output terminal of the second operational amplifier and the first input terminal of the second operational amplifier is coupled to the second output terminal of the first adjustable voltage divider.

Abstract: Examples for ultra-precise tuning of a selected memory cell are disclosed. In one example, a method of programming a first memory cell in a neural memory to a target value is disclosed, the method comprising programming a second memory cell by applying programming voltages to terminals of the second memory cell; and determining if an output of the first memory cell has reached the target value.

Abstract: A first example comprises programming a memory cell to store a value; applying a series of currents of increasing size to a bit line of the memory cell; and measuring a voltage of a control gate terminal of the memory cell to determine a bias. A second example comprises programming a memory cell to store a value; applying a predetermined current to a bit line of the memory cell; and measuring a voltage of a control gate terminal of the memory cell to determine a bias.

Abstract: Numerous embodiments are disclosed for an output circuit for an analog neural memory in a deep learning artificial neural network. In some embodiments, an output block receives current from a W+ bit line and current from an associated W? bit line, and the output block generates an output signal that is a differential signal in certain embodiments and is a single ended signal in other embodiments.

Abstract: Numerous examples are disclosed for performing calibration of various electrical parameters in a deep learning artificial neural network. In one example, a method comprises adjusting a bias voltage applied to one or more non-volatile memory cells in an artificial neural network, performing a performance target check on the one or more non-volatile memory cells in the artificial neural network, and repeating the adjusting and performing until the performance target check indicates an electrical parameter is within a predetermined range.

Abstract: A memory device includes a SOI substrate comprising bulk silicon, an insulation layer vertically over the bulk silicon, and a silicon layer vertically over the insulation layer. A memory cell includes source and drain regions formed in the bulk silicon with a channel region of the bulk silicon extending therebetween, and a floating gate which includes a first portion of the silicon layer disposed vertically over and insulated from a first portion of the channel region by the insulation layer. The first portion of the silicon layer is epitaxially thickened or a layer of polysilicon is formed on the first portion of the silicon layer. A select gate is disposed vertically over and insulated from a second portion of the channel region. A control gate is disposed vertically over and insulated from the floating gate. An erase gate is disposed vertically over and insulated from the source region.

Abstract: A method of forming memory cells, high voltage devices and logic devices on fins of a semiconductor substrate's upper surface, and the resulting memory device formed thereby. The memory cells are formed on a pair of the fins, where the floating gate is disposed between the pair of fins, the word line gate wraps around the pair of fins, the control gate is disposed over the floating gate, and the erase gate is disposed over the pair of fins and partially over the floating gate. The high voltage devices include HV gates that wrap around respective fins, and the logic devices include logic gates that are metal and wrap around respective fins.

Abstract: Examples of improved charge pumps are disclosed. In one example, a system comprises a first charge path comprising a first stage to boost an input voltage and a second stage to boost a voltage received from the first stage of the first charge path; and a second charge path comprising a first stage to boost an input voltage and a second stage to boost a voltage received from the first stage of the second charge path; wherein an output of the second stage of the first charge path is coupled to the first stage of the second charge path and an output of the second stage of the second charge path is coupled to the first stage of the first charge path.

Abstract: Numerous examples are disclosed of systems and methods to implement redundancy. In one example, a system comprises an array of non-volatile memory cells; a redundant array of non-volatile memory cells; and an input block coupled to respective rows in the array and respective rows in the redundant array and comprising row tag registers and redundant row tag registers.

Abstract: Numerous examples are disclosed of systems and methods to implement redundancy. In one example, a system comprises an array of non-volatile memory cells; a redundant array of non-volatile memory cells; and an input block coupled to respective rows in the array and respective rows in the redundant array and comprising row tag registers and redundant row tag registers.

Abstract: Numerous embodiments of input circuitry for an analog neural memory in a deep learning artificial neural network are disclosed.

Abstract: In one example, a system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells arranged in rows and columns; and a sigma-delta analog-to-digital converter to receive a current from a column of the vector-by-matrix multiplication array and to generate a digital output in response to the current.

Abstract: In one example, a method comprises programming a memory cell capable of storing any of N values with 1 of the N values; applying a series of currents of increasing size to a bit line of the memory cell; comparing a voltage of the bit line to a reference voltage to generate a comparison output; and when the comparison output changes value, measuring a voltage of a control gate terminal of the memory cell and storing the voltage in a bias lookup table.

Abstract: Numerous embodiments of an array of non-volatile memory cells are disclosed herein. In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns; wherein in a first mode, the array stores digital data; and wherein in a second mode, the array stores analog data.

Abstract: A method of programming a memory device comprising control gate lines, erase gate lines, select gate lines, source lines and bit lines connected to rows and columns of memory cells, the method comprising performing an erase operation that includes applying a first voltage to one of the erase gate lines, performing a pre-program operation that includes electrically connecting the one of the erase gate lines to a pair of the control gate lines to use positive charge on the one of the erase gate lines from the erase operation to pre-charge the pair of the control gate lines, and performing a program operation that includes applying a second voltage to the one of the erase gate lines and the pair of the control gate lines.

Abstract: A method of programming memory cells that includes reading the memory cells to determine respective read currents for the memory cells. Respective ones of the memory cells are assigned to one of a plurality of groups of the memory cells, wherein respective ones of the plurality of groups of the memory cells are associated with a different range of read currents, such that the determined read current for a respective one of the memory cells is within the range of read currents for the group of the memory cells to which the memory cell is assigned. The memory cells are programmed using program currents that vary for respective ones of the memory cells depending upon the group of the memory cells to which the memory cell is assigned.

Abstract: A method of scanning N×N pixels using a vector-by-matrix multiplication array by (a) associating a filter of M×M pixels adjacent first vertical and horizontal edges, (b) providing values for the pixels associated with different respective rows of the filter to input lines of different respective N input line groups, (c) shifting the filter horizontally by X pixels, (d) providing values for the pixels associated with different respective rows of the horizontally shifted filter to input lines, of different respective N input line groups, which are shifted by X input lines, (e) repeating steps (c) and (d) until a second vertical edge is reached, (f) shifting the filter horizontally to be adjacent the first vertical edge, and shifting the filter vertically by X pixels, (g) repeating steps (b) through (e) for the vertically shifted filter, and (h) repeating steps (f) and (g) until a second horizontal edge is reached.

Abstract: A method of operating memory cells includes programming memory cells at a first temperature to different program states associated with first read current values confirmed by using nominal read conditions. Modified read conditions are determined such that a second read current for the one memory cells at a second temperature is approximately equal to the first read current value for the one memory cell. A read operation is performed on the memory cells at the second temperature using the modified read conditions to determine respective third read current values. Error read current values are determined as respective differences between the first and third read current values. Upper and lower program states are assigned to respective desired program states, with read currents that correspond approximately to respective determined error read current values, and are separated approximately by a respective target read current value associated with the respective desired program state.