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: 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: 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: 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: Numerous embodiments of analog neural memory arrays are disclosed. Certain embodiments comprise an adaptive bias decoder for providing additional bias to array input lines to compensate for instances where ground floats above 0V. This is useful, for example, to minimize the voltage drop for a read, program, or erase operation while maintaining accuracy in the operation.

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: Numerous embodiments of analog neural memory arrays are disclosed. Two or more physical memory cells are grouped together to form a logical cell that stores one of N possible levels. Within each logical cell, the memory cells can be programmed using different mechanisms. For example, one or more of the memory cells in a logical cell can be programmed using a coarse programming mechanism, one or more of the memory cells can be programmed using a fine mechanism, and one or more of the memory cells can be programmed using a tuning mechanism. This achieves extreme programming accuracy and programming speed.

Abstract: Numerous embodiments of analog neural memory arrays are disclosed. Certain embodiments contain improved mechanisms for pulling source lines down to ground expeditiously. This is useful, for example, to minimize the voltage drop for a read, program, or erase operation.

Abstract: Various algorithms are disclosed for verifying the stored weight in a non-volatile memory cell in a neural network following a multilevel programming operation of the non-volatile memory cell by converting the stored weight into a plurality of digital output bits. Circuity, such as an adjustable reference current source, for implementing the algorithms are disclosed.

Abstract: Numerous embodiments for performing tuning of a page or a word of non-volatile memory cells in an analog neural memory are disclosed. High voltage circuits used to generate high voltages applied to terminals of the non-volatile memory cells during the precision tuning process are also disclosed. Programming sequences for the application of the voltages to the terminals to minimize the occurrence of disturbances during tuning are also disclosed.

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: Numerous embodiments of analog neural memory arrays are disclosed. In one embodiment, an analog neural memory system comprises an array of non-volatile memory cells, wherein the cells are arranged in rows and columns, the columns arranged in physically adjacent pairs of columns, wherein within each adjacent pair one column in the adjacent pair comprises cells storing W+ values and one column in the adjacent pair comprises cells storing W? values, wherein adjacent cells in the adjacent pair store a differential weight, W, according to the formula W=(W+)?(W?). In another embodiment, an analog neural memory system comprises a first array of non-volatile memory cells storing W+ values and a second array storing W? values.

Abstract: A method of controlling a memory device can include: receiving, by an interface, a write command from a host; beginning execution of a write operation on a first array plane of a memory array in response to the write command, where the memory array includes a plurality of memory cells arranged in a plurality of array planes; receiving, by the interface, a read command from the host; reconfiguring the write operation in response to detection of the read command during execution of the write operation; beginning execution of a read operation on a second array plane in response to the read command; and restoring the configuration of the write operation after the read operation has at least partially been executed.

Abstract: A memory device can include a plurality of banks, each bank including a memory cell array of nonvolatile (NV) memory cells; a plurality of charge pumps, including a first charge pump and second charge pump; and a switch circuit. The switch circuit can be configured to, in a first mode, connect the first charge pump to first circuits of the banks and isolate the second charge pump from the first circuits, and in a second mode, isolate the first charge pump from the first circuits and connect the second charge pump to the first circuits.

Abstract: Numerous embodiments for reading or verifying a value stored in a selected memory cell in a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one embodiment, an input comprises a set of input bits that result in a series of input signals applied to a terminal of the selected memory cell, further resulting in a series of output signals that are digitized, shifted based on the bit location of the corresponding input bit in the set of input bits, and added to yield an output indicating a value stored in the selected memory cell.