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: Memory cells formed on upwardly extending fins of a semiconductor substrate, each including source and drain regions with a channel region therebetween, a floating gate extending along the channel region and wrapping around the fin, a word line gate extending along the channel region and wrapping around the fin, a control gate over the floating gate, and an erase gate over the source region. The control gates are a continuous conductive strip of material. First and second fins are spaced apart by a first distance. Third and fourth fins are spaced apart by a second distance. The second and third fins are spaced apart by a third distance greater than the first and second distances. The continuous strip includes a portion disposed between the second and third fins, but no portion of the continuous strip is disposed between the first and second fins nor between the third and fourth fins.

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: A neural network device with synapses having memory cells each having source and drain regions in a semiconductor substrate with a channel region extending there between, a floating gate over an entirety of the channel region, and a first gate over the floating gate. First lines each electrically connect together the first gates in one of the memory cell rows, second lines each electrically connect together the source regions in one of the memory cell rows, and third lines each electrically connect together the drain regions in one of the memory cell columns. The synapses are configured to receive a first plurality of inputs as electrical voltages on the first lines or on the second lines, and to provide a first plurality of outputs as electrical currents on the third lines.

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: A method of forming a semiconductor device where memory cells and some logic devices are formed on bulk silicon while other logic devices are formed on a thin silicon layer over insulation over the bulk silicon of the same substrate. The memory cell stacks, select gate poly, and source regions for the memory devices are formed in the memory area before the logic devices are formed in the logic areas. The various oxide, nitride and poly layers used to form the gate stacks in the memory area are formed in the logic areas as well. Only after the memory cell stacks and select gate poly are formed, and the memory area protected by one or more protective layers, are the oxide, nitride and poly layers used to form the memory cell stacks removed from the logic areas, and the logic devices are then formed.

Abstract: Numerous embodiments are disclosed for providing temperature compensation in a an analog memory array. The analog memory array optionally is a vector-by-matrix multiplier in an analog neuromorphic memory system used in a deep learning neural network. One embodiment comprises measuring an operating temperature within a memory array and applying, by a temperature compensation block, a bias voltage to a terminal of a memory cell in the array, wherein the bias voltage is a function of the operating temperature.

Abstract: Memory cells formed on upwardly extending fins of a semiconductor substrate, each including source and drain regions with a channel region therebetween, a floating gate extending along the channel region and wrapping around the fin, a word line gate extending along the channel region and wrapping around the fin, a control gate over the floating gate, and an erase gate over the source region. The control gates are a continuous conductive strip of material. First and second fins are spaced apart by a first distance. Third and fourth fins are spaced apart by a second distance. The second and third fins are spaced apart by a third distance greater than the first and second distances. The continuous strip includes a portion disposed between the second and third fins, but no portion of the continuous strip is disposed between the first and second fins nor between the third and fourth fins.

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 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: 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: 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.

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: 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: 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 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, and second and third gates over the floating gate and over 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 third gates in one of the memory cell rows, fourth lines each electrically connect the source regions in one of the memory cell rows, and fifth 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, second or third lines, and provide a first plurality of outputs as electrical currents on the fifth lines.

Abstract: A method of forming a semiconductor device where memory cells and some logic devices are formed on bulk silicon while other logic devices are formed on a thin silicon layer over insulation over the bulk silicon of the same substrate. The memory cell stacks, select gate poly, and source regions for the memory devices are formed in the memory area before the logic devices are formed in the logic areas. The various oxide, nitride and poly layers used to form the gate stacks in the memory area are formed in the logic areas as well. Only after the memory cell stacks and select gate poly are formed, and the memory area protected by one or more protective layers, are the oxide, nitride and poly layers used to form the memory cell stacks removed from the logic areas, and the logic devices are then formed.

Abstract: Numerous embodiments are disclosed for providing temperature compensation and leakage compensation for an analog neuromorphic memory system used in a deep learning neural network. The embodiments for providing temperature compensation implement discreet or continuous adaptive slope compensation and renormalization for devices, reference memory cells, or selected memory cells in the memory system. The embodiments for providing leakage compensation within a memory cell in the memory system implement adaptive erase gate coupling or the application of a negative bias on a control gate terminal, a negative bias on a word line terminal, or a bias on a source line terminal.

Abstract: An artificial neural network device that utilizes analog neuromorphic memory that comprises one or more non-volatile memory arrays. The embodiments comprise improved mechanisms and algorithms for tuning the non-volatile memory arrays such that the floating gates of the memory cells can be quickly and accurately injected with the desired amount of charge to signify an analog value utilized as a weight by the artificial neural network.

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.