Abstract: A method of programing a memory device having a plurality of memory cell groups where each of the memory cell group includes N non-volatile memory cells, where N is an integer greater than or equal to 2. For each memory cell group, the method includes programming each of the non-volatile memory cells in the memory cell group to a particular program state, performing multiple read operations on each of the non-volatile memory cells in the memory cell group, identifying one of the non-volatile memory cells in the memory cell group that exhibits a lowest read variance during the multiple read operations, deeply programming all of the non-volatile memory cells in the memory cell group except the identified non-volatile memory cell, and programming the identified non-volatile memory cell in the memory cell group with user data.

Abstract: 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.

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 for reading 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 pulses applied to a terminal of the selected memory cell, further resulting in a series of output signals that are summed to determine the value stored in the selected memory cell. In another embodiment, an input comprises a set of input bits, where each input bit results in a single pulse or no pulse being applied to a terminal of the selected memory cell, further resulting in a series of output signals which are then weighted according to the binary bit location of the input bit, and where the weighted signals are then summed to determine the value stored in the selected memory cell.

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 for reading 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 pulses applied to a terminal of the selected memory cell, further resulting in a series of output signals that are summed to determine the value stored in the selected memory cell. In another embodiment, an input comprises a set of input bits, where each input bit results in a single pulse or no pulse being applied to a terminal of the selected memory cell, further resulting in a series of output signals which are then weighted according to the binary bit location of the input bit, and where the weighted signals are then summed to determine the value stored in the selected memory cell.

Abstract: 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: A simplified method for forming a non-volatile memory cell using two polysilicon depositions. A first polysilicon layer is formed on and insulated from the semiconductor substrate in a first polysilicon deposition process. An insulation block is formed on the first polysilicon layer. Spacers are formed adjacent first and second sides of the insulation block, and with the spacer adjacent the first side is reduced. Exposed portions of the first poly silicon layer are removed while maintaining a polysilicon block of the first polysilicon layer disposed under the insulation block. A second polysilicon layer is formed over the substrate and the insulation block in a second polysilicon deposition process. Portions of the second polysilicon layer are removed while maintaining a first polysilicon block (disposed adjacent the first side of the insulation block), and a second polysilicon block (disposed adjacent the second side of the insulation block).

Abstract: Numerous embodiments of circuitry for a set-while-verify operation and a reset-while verify operation for resistive random access memory cells are disclosed. In one embodiment, a set-while-verify circuit for performing a set operation on a selected RRAM cell in the array applies a combination of voltages or current to a bit line, word line, and source line associated with the selected RRAM cell and stops said applying when the set operation is complete. In another embodiment, a reset-while-verify circuit for performing a reset operation on a selected RRAM cell in the array applies a combination of voltages or current to a bit line, word line, and source line associated with the selected RRAM cell and stops said applying when the reset operation is complete.

Abstract: A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region disposed between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell rows, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell columns, and fourth lines each electrically connect the drain regions in one of the memory cell columns. The synapses receive a first plurality of inputs as electrical voltages on the first or second lines, and provide a first plurality of outputs as electrical currents on the third or fourth lines.

Abstract: 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.

Abstract: The present embodiments relate to systems and methods for implementing wear leveling in a flash memory device that emulates an EEPROM. The embodiments utilize an index array, which stores an index word for each logical address in the emulated EEPROM. The embodiments comprise a system and method for receiving an erase command and a logical address, the logical address corresponding to a sector of physical words of non-volatile memory cells in an array of non-volatile memory cells, the sector comprising a first physical word, a last physical word, and one or more physical words between the first physical word and the last physical word; when a current word, identified by an index bit, is the last physical word in the sector, erasing the sector; and when the current word is not the last physical word in the sector, changing a next index bit.

Abstract: 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.

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 forming a device on a substrate with recessed first/third areas relative to a second area by forming a fin in the second area, forming first source/drain regions (with first channel region therebetween) by first/second implantations, forming second source/drain regions in the third area (defining second channel region therebetween) by the second implantation, forming third source/drain regions in the fin (defining third channel region therebetween) by third implantation, forming a floating gate over a first portion of the first channel region by first polysilicon deposition, forming a control gate over the floating gate by second polysilicon deposition, forming an erase gate over the first source region and a device gate over the second channel region by third polysilicon deposition, and forming a word line gate over a second portion of the first channel region and a logic gate over the third channel region by metal deposition.

Abstract: Various embodiments of high voltage generation circuits, high voltage operational amplifiers, adaptive high voltage supplies, adjustable high voltage incrementor, adjustable reference supplies, and reference circuits are disclosed. These circuits optionally can be used for programming a non-volatile memory cell in an analog neural memory to store one of many possible values.

Abstract: Numerous embodiments are disclosed for accessing redundant non-volatile memory cells in place of one or more rows or columns containing one or more faulty non-volatile memory cells during a program, erase, read, or neural read operation in an analog neural memory system used in a deep learning artificial neural network.

Abstract: Numerous embodiments are disclosed of improved architectures for storing and retrieving system data in a non-volatile memory system. Using these embodiments, system data is much less likely to become corrupted due to charge loss, charge redistribution, disturb effects, and other phenomena that have caused corruption in prior art non-volatile memory systems.

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 are disclosed for providing temperature compensation in an analog memory array. A method and related system are disclosed for compensating for temperature changes in an array of memory cells by measuring an operating temperature within the array of memory cells and changing a threshold voltage of a selected memory cell in the array of memory cells to compensate for a change in the operating temperature.