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: A method of forming memory cells, high voltage devices and logic devices on fins of a semiconductor substrate's upper surface, and the resulting memory device formed thereby. The memory cells are formed on a pair of the fins, where the floating gate is disposed between the pair of fins, the word line gate wraps around the pair of fins, the control gate is disposed over the floating gate, and the erase gate is disposed over the pair of fins and partially over the floating gate. The high voltage devices include HV gates that wrap around respective fins, and the logic devices include logic gates that are metal and wrap around respective fins.

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: 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 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: 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 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: A method of testing non-volatile memory cells formed on a die includes erasing the memory cells and performing a first read operation to determine a lowest read current RC1 for the memory cells and a first number N1 of the memory cells having the lowest read current RC1. A second read operation is performed to determine a second number N2 of the memory cells having a read current not exceeding a target read current RC2. The target read current RC2 is equal to the lowest read current RC1 plus a predetermined current value. The die is determined to be acceptable if the second number N2 is determined to exceed the first number N1 plus a predetermined number. The die is determined to be defective if the second number N2 is determined not to exceed the first number N1 plus the predetermined number.

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: Numerous examples of an input function circuit block and an output neuron circuit block coupled to a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one example, an artificial neural network comprises a vector-by-matrix multiplication array comprising a plurality of non-volatile memory cells organized into rows and columns; an input function circuit block to receive digital input signals, convert the digital input signals into analog signals, and apply the analog signals to control gate terminals of non-volatile memory cells in one or more rows of the array during a programming operation; and an output neuron circuit block to receive analog currents from the columns of the array during a read operation and generate an output signal.

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: Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. In one example, a method comprises programming a plurality of analog neural non-volatile memory cells in an array of analog neural non-volatile memory cells to store one of N different values, where N is a number of different levels that can be stored in any of the analog neural non-volatile memory cells; measuring a current drawn by the plurality of analog neural non-volatile memory cells; comparing the measured current to a target value; and identifying the plurality of the analog neural non-volatile memory cells as bad if the difference between the measured value and the target value exceeds a threshold.

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: Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. In one example, a method comprises programming an analog neural non-volatile memory cell in an array to a target value representing one of N different values, where N is an integer; verifying that a value stored in the analog neural non-volatile memory cell is within an acceptable window of values around the target value; repeating the programming and verifying for each of the N values; and identifying the analog neural non-volatile memory cell as bad if any of the verifying indicates a value stored in the cell outside of the acceptable window of values around the target value.

Abstract: Numerous embodiments are disclosed for compensating for differences in the slope of the current-voltage characteristic curve among reference transistors, reference memory cells, and flash memory cells during a read operation in an analog neural memory in a deep learning artificial neural network. In one embodiment, a method comprises receiving a first voltage, multiplying the first voltage by a coefficient to generate a second voltage, applying the first voltage to a gate of one of a reference transistor and a selected memory cell, applying the second voltage to a gate of the other of a reference transistor and a selected memory cell, and using the reference transistor in a sense operation to determine a value stored in the selected memory cell.

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 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. Compensation measures can be utilized that compensate for changes in voltage or current as the number of cells being programmed changes.

Abstract: Numerous embodiments are disclosed for compensating for differences in the slope of the current-voltage characteristic curve among reference transistors, reference memory cells, and flash memory cells during a read operation in an analog neural memory in a deep learning artificial neural network. In one embodiment, a method comprises receiving an input voltage, multiplying the input voltage by a coefficient to generate an output voltage, applying the output voltage to a gate of a selected memory cell, performing a sense operating using the selected memory cell and a reference device to determine a value stored in the selected memory cell, wherein a slope of a current-voltage characteristic curve of the reference device and a slope of the current-voltage characteristic curve of the selected memory cell are approximately equal during the sense operation.

Abstract: Numerous examples 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. In one example, a method for performing a read or verify operation in a vector-by-matrix multiplication system comprising an input function circuit, a memory array, and an output circuit block is disclosed, the method comprising receiving, by the input function circuit, digital bit input values; converting the digital input values into an input signal; applying the input signal to control gate terminals of selected cells in the memory array; and generating, by the output circuit block, an output value in response to currents received from the memory array.

Abstract: Numerous examples of a precision programming 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. In one example, a neuron output circuit for providing a current to program as a weight value in a selected memory cell in a vector-by-matrix multiplication array is disclosed, the neuron output circuit comprising a first adjustable current source to generate a scaled current in response to a neuron current to implement a positive weight, and a second adjustable current source to generate a scaled current in response to a neuron current to implement a negative weight.