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.

A system for programming memory devices in an array is provided. The system may include a plurality of memory cells that are organized into an array having two or more rows of memory cells arranged horizontally and two or more columns of memory cells arranged vertically. The system may also include a current-compliance circuit that is electrically coupled to one or more memory cells in the plurality of memory cells. The current-compliance circuit may be configured to limit an amount of current supplied to the one or more memory cells during a programming phase of the one or more memory cells.

Numerous embodiments of decoders for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The decoders include bit line decoders, word line decoders, control gate decoders, source line decoders, and erase gate decoders. In certain embodiments, a high voltage version and a low voltage version of a decoder is used.

A multi-time programming non-volatile memory includes a select transistor, a floating gate transistor, a switch transistor, a capacitor and an erase gate element. The select transistor is connected with a select line and a source line. The floating gate transistor includes a floating gate. The floating gate transistor is connected with the select transistor. The switch transistor is connected with a word line, the floating gate transistor and a bit line. A first terminal of the capacitor is connected with the floating gate. A second terminal of the capacitor is connected with a control line. The erase gate element includes the floating gate, a gate oxide layer and a p-type region. The erase gate element is connected with an erase line. The floating gate of the erase gate element at least includes an n-type floating gate part.

Numerous embodiments of a precision programming 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.

A memory structure including a substrate, a gate structure, a charge storage layer, and a first control gate is provided. The substrate has a fin portion. A portion of the gate structure is disposed on the fin portion. The gate structure and the fin portion are electrically insulated from each other. The charge storage layer is coupled the gate structure. The charge storage layer and the gate structure are electrically insulated from each other. The first control gate is coupled to the charge storage layer. The first control gate and the charge storage layer are electrically insulated from each other.

A memory device, and method of making the same, that includes a substrate of semiconductor material of a first conductivity type, first and second regions spaced apart in the substrate and having a second conductivity type different than the first conductivity type, with a first channel region in the substrate extending between the first and second regions, a first floating gate disposed over and insulated from a first portion of the first channel region adjacent to the second region, a first coupling gate disposed over and insulated from the first floating gate, a first word line gate disposed over and insulated from a second portion of the first channel region adjacent the first region, and a first erase gate disposed over and insulated from the first word line gate.

A memory device includes a non-volatile memory cells, source regions and drain regions arranged in rows and columns. Respective ones of the columns of drain regions include first drain regions and second drain regions that alternate with each other. Respective ones of first lines electrically connect together the source regions in one of the rows of the source regions and are electrically isolated from the source regions in other rows of the source regions. Respective ones of second lines electrically connect together the first drain regions of one of the columns of drain regions and are electrically isolated from the second drain regions of the one column of drain regions. Respective ones of third lines electrically connect together the second drain regions of one of the columns of drain regions and are electrically isolated from the first drain regions of the one column of drain regions.

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.

In one example, a method comprises performing a first programming process on a selected non-volatile memory cell, the first programming process comprising a plurality of program-verify cycles, wherein a programming voltage duration of increasing period is applied to one of a floating gate, a control gate terminal, an erase gate terminal, and a source line terminal of the selected non-volatile memory cell in each program-verify cycle after the first program-verify cycle.