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 embodiments are disclosed for splitting an array of non-volatile memory cells in an analog neural memory in a deep learning artificial neural network into multiple parts. Each part of the array interacts with certain circuitry dedicated to that part and with other circuitry that is shared with one or more other parts of the array.

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 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: 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: 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. 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: Various embodiments of word line decoders, control gate decoders, bit line decoders, low voltage row decoders, and high voltage row decoders and various types of physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. Shared and segmented embodiments of high voltage row decoders are disclosed.

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: 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: Embodiments for ultra-precise tuning of a selected memory cell are disclosed. The selected memory cell optionally is first programmed using coarse programming and fine programming methods. The selected memory cell then undergoes ultra-precise programming through the programming of an adjacent memory cell. As the adjacent memory cell is programmed, capacitive coupling between the floating gate of the adjacent memory cell and the floating gate of the selected memory cell will cause the voltage of the floating gate of the selected memory cell to increase, but in smaller increments than could be achieved by programming the selected memory cell directly. In this manner, the selected memory cell can be programmed with ultra-precise gradations.

Abstract: Numerous embodiments for reading or verifying a value stored in a selected non-volatile memory cell in a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The embodiments comprise various designs of input blocks for applying inputs to the VMM array during a read or verify operation and various designs of output blocks for receiving outputs from the VMM array during the read or verify operation.

Abstract: A non-volatile memory device is disclosed. The non-volatile memory device comprises an array of flash memory cells comprising a plurality of flash memory cells organized into rows and columns, wherein the array is further organized into a plurality of sectors, each sector comprising a plurality of rows of flash memory cells, and a row driver selectively coupled to a first row and a second row.

Abstract: Numerous embodiments for processing the current output of a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The embodiments comprise a summer circuit and an activation function circuit. The summer circuit and/or the activation function circuit comprise circuit elements that can be adjusted in response to the total possible current received from the VMM to optimize power consumption.

Abstract: Numerous embodiments of analog neural memory arrays are disclosed. In certain embodiments, each memory cell in the array has an approximately constant source impedance when that cell is being operated. In certain embodiments, power consumption is substantially constant from bit line to bit line within the array when cells are being read. In certain embodiments, weight mapping is performed adaptively for optimal performance in power and noise.

Abstract: Various embodiments of tandem row decoders are disclosed. Each embodiment of a tandem row decoder comprises a word line decoder and a control gate decoder. The tandem row decoder exhibits reduced leakage current on the word line and the control gate line when the tandem row decoder is not enabled.

Abstract: Various embodiments of tandem row decoders are disclosed. Each embodiment of a tandem row decoder comprises a word line decoder and a control gate decoder. The tandem row decoder exhibits reduced leakage current on the word line and the control gate line when the tandem row decoder is not enabled.

Abstract: The present invention relates to a flash memory cell with only four terminals and a high voltage row decoder for operating an array of such flash memory cells. The invention allows for fewer terminals for each flash memory cell compared to the prior art, which results in a simplification of the decoder circuitry and overall die space required per flash memory cells. The invention also provides for the use of high voltages on one or more of the four terminals to allow for read, erase, and programming operations despite the lower number of terminals compared to prior art flash memory cells.