Implementation of Ultra Micro Boltzmann Machine Neuron

我们研究了利用单电子隧穿（SET）电路解决神经网络问题的方法，并得到了以下结果：1）玻尔兹曼机器神经元的简单电路：具有随机响应的玻尔兹曼机器神经元的电路通常是复杂的。然而，我们发现，通过使用单电子隧穿电路，我们可以构建一个紧凑的神经元电路，其中库仑阻塞条件进行适当的调整，它可以产生一个随机的1-0比特流的输出与概率为1的输出由输入信号控制。可以构造由两个以上神经元组成的网络。通过控制偏置电压，可以实现网络的退火操作。2）消除局部最小值问题的方法：利用单电子电路中的共隧穿现象，可以得到不存在局部最小值问题的神经网络。在该网络中，两个或多个阈值元件可以在相干组合中同时改变它们的输出。因此，可以发生具有大汉明距离的状态转移，并且局部最小困难可以消失。因此，总是可以实现全局最小能量状态。利用这一特性，可以开发新的计算设备，解决组合问题，而不受局部最小困难的阻碍。

We have investigated ways of using a single-electron-tunneling (SET) circuit to solve problems in neural networks, and have obtained the following results :1) A simple circuit for a Boltzmann machine neuron : The circuit for the Boltzmann machine neuron that has a stochastic response is usually complicated. We found, however, that by using a single-electron-tunneling circuit, we can construct a compact neuron circuit in which the Coulomb blockade condition is adjusted appropriately, It can produce an output of a random 1-0 bit stream with the probability for an output of 1 controlled by an input signal. A network consisting of more than two neurons can be constructed. So called 'annealing' operation of the network is available by controlling the bias voltage.2) A way to eliminate the local-minimum problem : If we utilize the cotunneling phenomenon found in single-electron circuits, we can obtain a neural network without the local-minimum problem. In this network, two or more threshold elements can change their outputs simultaneously in a coherent combination. Therefore a state transition with a large Hamming distance can occur and the local-minimum difficulty can disappear. As a result, the global-minimum energy state can always be achieved. Use of this property makes possible the development of novel computation devices that solve combinatorial problems without hindrance from the local-minimum difficulty.