Fast and Accurate Prediction of Electrical Characteristics of Next-Generation Node 3-D NAND Flash Memory Using Transfer Learning

Electrical characteristics of scaled 3-D NAND cells for next-generation node development were predicted using transfer learning with limited data. The NAND cell structure parameters were considered as the inputs, and outputs included key electrical characteristics, such as cell <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>t</i></sub>, the difference in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>t</i></sub> between the initial and programming states (Δ<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>t</i></sub>), subthreshold swing (SS), and ON-current (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>ON</roman></sub>). A multilayer perceptron (MLP) model comprising four hidden layers and focusing on large NAND cells (25 nm gate length) with 2000 data points served as a pre-trained model. The transfer model leveraged pre-trained knowledge to predict the electrical characteristics of smaller cells (19 nm gate length) with 500 data points without weight and bias training. Evaluation of test data exhibited remarkable accuracy with both the mean and standard deviation below 3%, proving the model’s effectiveness despite limited data. In addition, a comprehensive evaluation was conducted by comparing the performance of the model with variations in the dataset size and the presence of transfer learning, highlighting the effectiveness and advantages of transfer learning. Transfer learning could provide detailed structure information of the next node for engineers and expedite device development, resulting in significant time and cost savings.