Abstract: Numerous embodiments are disclosed of a non-volatile memory cell array formed in a p-well, which is formed in a deep n-well, which is formed in a p-substrate. During an erase operation, a negative voltage is applied to the p-well, which reduces the peak positive voltage required to be applied to the cells to cause the cells to erase.

Abstract: The present invention relates to a flash memory device that uses strap cells in a memory array of non-volatile memory cells as source line pull down circuits. In one embodiment, the strap cells are erase gate strap cells. In another embodiment, the strap cells are source line strap cells. In another embodiment, the strap cells are control gate strap cells. In another embodiment, the strap cells are word line strap cells.

Abstract: Numerous embodiments of a precision tuning algorithm and 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. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: Configurable input blocks and output blocks and physical layouts are disclosed for analog neural memory systems that utilize non-volatile memory cells. An input block can be configured to support different numbers of arrays arranged in a horizontal direction, and an output block can be configured to support different numbers of arrays arranged in a vertical direction. Adjustable components are disclosed for use in the configurable input blocks and output blocks.

Abstract: Numerous embodiments are disclosed for converting neuron current output by a vector-by-matrix multiplication (VMM) array into neuron current-based time pulses and providing such pulses as an input to another VMM array within an artificial neural network. Numerous embodiments are disclosed for converting the neuron current-based time pulses into analog current or voltage values if an analog input is needed for the VMM array.

Abstract: A method of forming a memory cell includes forming a first polysilicon block over an upper surface of a semiconductor substrate and having top surface and a side surface meeting at a sharp edge, forming an oxide layer with a first portion over the upper surface, a second portion directly on the side surface, and a third portion directly on the sharp edge, performing an etch that thins the oxide layer in a non-uniform manner such that the third portion is thinner than the first and second portions, performing an oxide deposition that thickens the first, second and third portions of the oxide layer, wherein after the oxide deposition, the third portion is thinner than the first and second portions, and forming a second polysilicon block having one portion directly on the first portion of the oxide layer and another portion directly on the third portion of the oxide layer.

Abstract: Numerous embodiments of a precision tuning algorithm and 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. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: Numerous examples for performing tuning of a page or a word of non-volatile memory cells in an analog neural memory are disclosed. In one example, an analog neural memory system comprises an array of non-volatile memory cells arranged into rows and columns, each non-volatile memory cell comprising a word line terminal, a bit line terminal, and an erase gate terminal; a plurality of word lines, each word line coupled to word line terminals of a row of non-volatile memory cells; a plurality of bit lines, each bit line coupled to bit line terminals of a column of non-volatile memory cells; and a plurality of erase gate enable transistors, each erase gate enable transistor coupled to erase gate terminals of a word of non-volatile memory cells.

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: Numerous examples for performing tuning of a page or a word of non-volatile memory cells in an analog neural memory are disclosed. In one example, a method comprises programming a word or page of non-volatile memory cells in an analog neural memory system; and identifying any fast bits in the word or page of non-volatile memory cells.

Abstract: Circuitry and methods are disclosed for compensating for leakage in analog neural memory in deep learning artificial neural networks. In one example, a method is disclosed of compensating for leakage in an array of analog neural non-volatile memory cells, wherein the array is arranged in rows and columns, wherein each row is coupled to a word line and each column is coupled to a bitline, the method comprising measuring leakage for a column of analog neural non-volatile memory cells coupled to a bitline; storing the measured leakage value; and applying the measured leakage value during a read operation of the column of analog neural non-volatile memory cells to compensate for the leakage.

Abstract: Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. In one example, a method is disclosed of testing a plurality of non-volatile memory cells in an array of non-volatile memory cells, wherein the array is arranged in rows and columns, wherein each row is coupled to a word line and each column is coupled to a bit line, and wherein each word line is selectively coupled to a row decoder and each bit line is selectively coupled to a column decoder, the method comprising asserting, by the row decoder, all word lines in the array; asserting, by the column decoder, all bit lines in the array; performing a deep programming operation on the array of non-volatile memory cells; and measuring a total current received from the bit lines.

Abstract: A memory device with memory cell pairs each having a single continuous channel region, first and second floating gates over first and second portions of the channel region, an erase gate over a third portion of the channel region between the first and second channel region portions, and first and second control gates over the first and second floating gates. For each of the pairs of memory cells, the first region is electrically connected to the second region of an adjacent pair of memory cells in the same active region, and the second region is electrically connected to the first region of an adjacent pair of the memory cells in the same active region.

Abstract: Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. The analog neural memory comprises one or more arrays of non-volatile memory cells. The testing circuitry and methods can be utilized during sort tests, qualification tests, and other tests to verify programming operations of one or more cells.

Abstract: A method of forming a memory device that includes forming a first insulation layer, a first conductive layer, and a second insulation layer on a semiconductor substrate, forming a trench in the second insulation layer to expose the upper surface of the first conductive layer, performing an oxidation process and a sloped etch process to reshape the upper surface to a concave shape, forming a third insulation layer on the reshaped upper surface, forming a conductive spacer on the third insulation layer, removing portions of the first conductive layer leaving a floating gate under the conductive spacer with the reshaped upper surface terminating at a side surface at a sharp edge, and forming a word line gate laterally adjacent to and insulated from the floating gate. The conductive spacer includes a lower surface that faces and matches the shape of the reshaped upper surface.

Abstract: Numerous embodiments are disclosed for compensating for differences in the slope of the current-voltage characteristic curve among reference transistors, reference memory cells, and flash memory cells during a read operation in an analog neural memory in a deep learning artificial neural network. The embodiments are able to compensate for slope differences during both sub-threshold and linear operation of reference transistors.

Abstract: A method of forming a semiconductor device by recessing the upper surface of a semiconductor substrate in first and second areas but not a third area, forming a first conductive layer in the first and second areas, forming a second conductive layer in all three areas, removing the first and second conductive layers from the second area and portions thereof from the first area resulting in pairs of stack structures each with a control gate over a floating gate, forming a third conductive layer in the first and second areas, forming a protective layer in the first and second areas and then removing the second conductive layer from the third area, then forming blocks of conductive material in the third area, then etching in the first and second areas to form select and HV gates, and replacing the blocks of conductive material with blocks of metal material.

Abstract: A method of forming a semiconductor device by recessing the upper surface of a semiconductor substrate in first and second areas but not a third area, forming a first conductive layer in the three areas, forming a second conductive layer in all three areas, removing the first and second conductive layers from the second area and portions thereof from the first area resulting in pairs of stack structures each with a control gate over a floating gate, forming a third conductive layer in all three areas, forming a protective layer in the first and second areas and then removing the third conductive layer from the third area, then forming blocks of dummy conductive material in the third area, then etching in the first and second areas to form select and HV gates, and then replacing the blocks of dummy conductive material with blocks of metal material.

Abstract: Numerous embodiments are disclosed for a high voltage generation algorithm and system for generating high voltages necessary for a particular programming operation in analog neural memory used in a deep learning artificial neural network. In one example, a method for programming a plurality of non-volatile memory cells in an array of non-volatile memory cells, comprises generating a high voltage, and programming a plurality of non-volatile memory cells in an array using the high voltage when a programming enable signal is asserted and providing a feedback loop to maintain the high voltage while programming the plurality of non-volatile memory cells.

Abstract: Numerous embodiments of power management techniques are disclosed for various operations involving one or more vector-by-matrix multiplication (VMM) arrays within an artificial neural network.