IC neuro chip with analog synapses for a correct reaction neural network

我们制作了一个具有脉冲表示的神经电路的微芯片。神经元输出是电压脉冲序列。突触是一个恒流源，其输出与神经元输出的占空比成比例。通过RC电路收集突触电流来对膜电位充电，提供类似于生物神经系统的模拟操作。我们采用4位SRAM作为突触权值的存储器，并研制了一种新型的浮栅模拟存储器（Switched Diffusion Analog Memory），它是集成神经网络中存储突触权值的关键器件。该模拟存储器包括隧道结、连接TFT、两个电容器和MOSFET，通过使用多晶硅氧化物膜作为隧道势垒实现的小电容与TFT的开关一起改善了线性度。最后，我们设计并制作了一种基于SDAM的模拟DBM（DeterministicBoltzmannMachinelearning）芯片，采用Hebbian规则设计的DBM学习电路适用于基于SDAM的集成模拟神经元芯片，可以同时工作于学习模式和检索模式。

We have fabricated a microchip of a neural circuit with pulse representation. The neuron output is a voltage pulse train. The synapse is a constant current source whose output is proportional to the duty ratio of neuron output. Membrane potential is charged by collection of synaptic currents through a RC circuit, providing an analog operation similar to the biological neural systems. We use a 4-bit SRAM as the memory for synaptic weights.Then, we have fabricated a new type of analog memory (Switched Diffusion Analog Memory) with a floating gate witch is used as a key component to store synaptic weights for integrated artificial neural networks. The analog memory comprises a tunnel junction, a connection TFT,two capacitors, and a MOSFET.A small capacitance realized by use of poly-Si oxide film as the tunneling barrier together with the switching of the TFT improves linearity. The experimental result agrees with the theoretical estimation.The last, we designed and fabricated a new analog DBM (Deterministic Boltzmann Machine learning) chip using SDAM.DBM learning circuit designed by Hebbian rule is suitable for integrated analog neuro-chip by use of SDAM which can be operated simultaneously for learning mode and retrieving mode.