Precision tuning for the programming of analog neural memory in a deep learning artificial neural network
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.
1. A method of programming a selected non-volatile memory cell to store one of N possible values, where N is an integer greater than 2, the selected non-volatile memory cell comprising a floating gate, the method comprising:
performing a coarse programming process comprising:
selecting one of M different current values as a first threshold current value, where M<N;
adding charge to the floating gate; and
repeating the adding step until a current through the selected non-volatile memory cell during a verify operation is less than or equal to the first threshold current value; and
performing a precision programming process until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a second threshold current value.
2. The method of claim 1 , further comprising:
performing a second precision programming process until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a third threshold current value.
3. The method of claim 1 , wherein the precision programming process comprises applying voltage pulses of increasing magnitude to the control gate of the selected non-volatile memory cell.
4. The method of claim 1 wherein the precision programming process comprises applying voltage pulses of increasing duration to the control gate of the selected non-volatile memory cell.
5. The method of claim 2 , wherein the second precision programming process comprises applying voltage pulses of increasing magnitude to the control gate of the selected non-volatile memory cell.
6. The method of claim 2 , wherein the second precision programming process comprises applying voltage pulses of increasing duration to the control gate of the selected non-volatile memory cell.
7. The method of claim 1 , wherein the selected non-volatile memory cell comprises a floating gate.
8. The method of claim 7 , wherein the selected non-volatile memory cell is a split-gate flash memory cell.
9. The method of claim 1 , wherein the selected non-volatile memory cell is in a vector-by-matrix multiplication array in an analog memory deep neural network.
10. The method of claim 1 , further comprising:
before performing the coarse programming process:
programming the selected non-volatile memory cell to a ‘0’ state; and
erasing the selected non-volatile memory cell to a weakly-erased level.
11. The method of claim 1 , further comprising:
before performing the coarse programming process:
erasing the selected non-volatile memory cell to a ‘1’ state; and
programming the selected non-volatile memory cell to a weakly-programmed level.
12. The method of claim 1 , further comprising:
performing a read operation on the selected non-volatile memory cell;
integrating the current drawn by the selected non-volatile memory cell during the read operation using an integrating analog-to-digital converter to generate digital bits.
13. The method of claim 1 , further comprising:
performing a read operation on the selected non-volatile memory cell;
converting the current drawn by the selected non-volatile memory cell during the read operation into digital bits using a sigma delta analog-to-digital converter.
14. A method of programming a selected non-volatile memory cell to store one of N possible values, where N is an integer greater than 2, the selected non-volatile memory cell comprising a floating gate and a control gate, the method comprising:
performing a coarse programming process comprising:
determining a slope value based on a change in voltage of the control gate of the selected non-volatile memory cell and a change in current drawn by the selected non-volatile memory cell;
determining a next programming voltage value based on the slope value;
adding an amount of charge from the floating gate of the selected non-volatile memory cell until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a first threshold current value; and
performing a precision programming process until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a second threshold current value.
15. The method of claim 14 , further comprising:
performing a second precision programming process until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a third threshold current value.
16. The method of claim 14 , wherein the precision programming process comprises applying voltage pulses of increasing magnitude to the control gate of the selected non-volatile memory cell.
17. The method of claim 14 , wherein the precision programming process comprises applying voltage pulses of increasing duration to the control gate of the selected non-volatile memory cell.
18. The method of claim 15 , wherein the precision programming process comprises applying voltage pulses of increasing magnitude to the control gate of the selected non-volatile memory cell.
19. The method of claim 15 , wherein the precision programming process comprises applying voltage pulses of increasing duration to the control gate of the selected non-volatile memory cell.
20. The method of claim 14 , wherein the step of determining a slope value comprises:
applying a programming voltage to the control gate of the selected non-volatile memory cell;
applying a first current through the selected non-volatile memory cell and determining a first voltage of the control gate;
applying a second current through the selected non-volatile memory cell and determining a second voltage of the control gate; and
calculating the slope value by dividing the difference between the second voltage and the first voltage by the difference between the second current and the first current.
21. The method of claim 14 , wherein the selected non-volatile memory cell is a split-gate flash memory cell.
22. The method of claim 14 , wherein the selected non-volatile memory cell is in a vector-by-matrix multiplication array in an analog memory deep neural network.
23. The method of claim 14 , further comprising:
before performing the coarse programming process:
programming the selected non-volatile memory cell to a ‘0’ state; and
erasing the selected non-volatile memory cell to a weakly-erased level.
24. The method of claim 14 , further comprising:
before performing the coarse programming process:
erasing the selected non-volatile memory cell to a ‘1’ state; and
programming the selected non-volatile memory cell to a weakly-programmed level.
25. The method of claim 14 , further comprising:
performing a read operation on the selected non-volatile memory cell;
integrating the current drawn by the selected non-volatile memory cell during the read operation using an integrating analog-to-digital converter to generate digital bits.
26. The method of claim 14 , further comprising:
performing a read operation on the selected non-volatile memory cell;
converting the current drawn by the selected non-volatile memory cell during the read operation into digital bits using a sigma delta analog-to-digital converter.
27. A method of programming a selected non-volatile memory cell to store one of N possible values, where N is an integer greater than 2, the selected non-volatile memory cell comprising a floating gate and a control gate, the method comprising:
performing a coarse programming process comprising:
applying a programming voltage to the control gate of the selected non-volatile memory cell;
repeating the applying step and increasing the programming voltage by an incremental voltage each time the applying step is performed until a current through the selected non-volatile memory cell during a verify operation is less than or equal to the threshold current value; and
performing a precision programming process until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a second threshold current value.
28. The method of claim 27 , further comprising:
performing a precision programming process until a current through the selected non-volatile memory cell during a verify operation is less than or equal to a third threshold current value.
29. The method of claim 27 , wherein the precision programming process comprises applying voltage pulses of increasing magnitude to the control gate of the selected non-volatile memory cell.
30. The method of claim 27 , wherein the prevision programming process comprises applying voltage pulses of increasing duration to the control gate of the selected non-volatile memory cell.
31. The method of claim 28 , wherein the precision programming process comprises applying voltage pulses of increasing magnitude to the control gate of the selected non-volatile memory cell.
32. The method of claim 28 , wherein the prevision programming process comprises applying voltage pulses of increasing duration to the control gate of the selected non-volatile memory cell.
33. The method of claim 27 , wherein the selected non-volatile memory cell comprises a floating gate.
34. The method of claim 33 , wherein the selected non-volatile memory cell is a split-gate flash memory cell.
35. The method of claim 27 , wherein the selected non-volatile memory cell is in a vector-by-matrix multiplication array in an analog memory deep neural network.
36. The method of claim 27 , further comprising:
before performing the coarse programming process:
programming the selected non-volatile memory cell to a ‘0’ state; and
erasing the selected non-volatile memory cell to a weakly-erased level.
37. The method of claim 27 , further comprising:
before performing the coarse programming process:
erasing the selected non-volatile memory cell to a ‘1’ state; and
programming the selected non-volatile memory cell to a weakly-programmed level.
38. The method of claim 27 , further comprising:
performing a read operation on the selected non-volatile memory cell;
integrating the current drawn by the selected non-volatile memory cell during the read operation using an integrating analog-to-digital converter to generate digital bits.
39. The method of claim 27 , further comprising:
performing a read operation on the selected non-volatile memory cell;
converting the current drawn by the selected non-volatile memory cell during the read operation into digital bits using a sigma delta analog-to-digital converter.
40. A method of reading a selected non-volatile memory cell storing one of N possible values, where N is an integer greater than 2, the method comprising:
applying digital input pulses to the selected non-volatile memory cell;
determining a value stored in the selected non-volatile memory cell based on an output of the selected non-volatile memory cell in response to each of the digital input pulses.
41. The method of claim 40 , wherein the number of digital input pulses corresponds to binary values.
42. The method of claim 40 , wherein the number of digital input pulses corresponds to digital bit position values.
43. The method of claim 40 , wherein the determining step comprises receiving an output neuron in an integrating analog-to-digital converter to generate digital bits indicating the value stored in the non-volatile memory cell.
44. The method of claim 40 , wherein the determining step comprises receiving an output neuron in an successive approximation register analog-to-digital converter to generate digital bits indicating the value stored in the non-volatile memory cell.
45. The method of claim 40 , wherein the output is current.
46. The method of claim 40 , wherein the output is charge.
47. The method of claim 40 , wherein the output is digital bits.
48. The method of claim 40 , wherein the selected non-volatile memory cell comprises a floating gate.
49. The method of claim 48 , wherein the selected non-volatile memory cell is a split-gate flash memory cell.
50. The method of claim 40 , wherein the selected non-volatile memory cell is in a vector-by-matrix multiplication array in an analog memory deep neural network.
51. A method of reading a selected non-volatile memory cell storing one of N possible values, where N is an integer greater than 2, the method comprising:
applying inputs to the selected non-volatile memory cell;
determining a value stored in the selected non-volatile memory cell based on an output of the selected non-volatile memory cell using an analog-to-digital converter circuit in response to the inputs.
52. The method of claim 51 , wherein the inputs are digital inputs.
53. The method of claim 51 , wherein the inputs are analog inputs.
54. The method of claim 51 , wherein the determining step comprises receiving an output neuron in an integrating single or dual slope analog-to-digital converter and generating digital bits indicating the value stored in the non-volatile memory cell.
55. The method of claim 51 , wherein the determining step comprises receiving an output neuron in an SAR analog-to-digital converter to generate digital bits indicating the value stored in the non-volatile memory cell.
56. The method of claim 51 , wherein the determining step comprises receiving an output neuron in an sigma delta analog-to-digital converter to generate digital bits indicating the value stored in the non-volatile memory cell.
57. The method of claim 51 , wherein the selected non-volatile memory cell comprises a floating gate.
58. The method of claim 51 , wherein the selected non-volatile memory cell is a split-gate flash memory cell.
59. The method of claim 51 , wherein the selected non-volatile memory cell is in a vector-by-matrix multiplication array in an analog memory deep neural network.
60. The method of claim 51 , wherein the selected non-volatile memory cell operates in sub-threshold region.
61. The method of claim 51 , wherein the selected non-volatile memory cell operates in linear region.