from src.core.afm import AFM as AFM from src.core.afm import CHECKER as CHECKER import src.core.analysis import src.core.report import src.core.tiff from time import sleep import numpy as np import os class Wafer_Zero_Scan(): def __init__(self): self.afm = AFM() self.base_dir = '' self.sub_dir = 'Wafer Zero Scan' self.file_name = 'RAW' self.parameter = None def move_to(self): pass def set(self): if self.parameter['xy_servo_enable'] == 'On': self.sub_dir += '/XYservo_On' self.parameter['z_scanner_range'] = 70 self.parameter['xy_scanner_range'] = 100 self.afm.enable_xy_servo() elif self.parameter['xy_servo_enable'] == 'Off': self.sub_dir += '/XYservo_Off' self.log_name = 'Position_XY.csv' self.parameter['z_scanner_range'] = 20 self.parameter['xy_scanner_range'] = 20 self.afm.enable_xy_servo('off') self.header = 'Repeat, Lable , Input X Pos., Input Y Pos., Output X Pos., Output Y Pos.\n ' if self.parameter['lift_enable'] == 'On': self.sub_dir += '_Lift_On' elif self.parameter['lift_enable'] == 'Off': self.sub_dir += '_Lift_Off' dir_path = self.base_dir + '\\' + self.sub_dir self.log_path = dir_path + '\\' + self.log_name if not os.path.exists(dir_path):os.makedirs(dir_path) fd = open(self.log_path,'w') fd.write(self.header) fd.close() self.afm.set_data_location(self.base_dir, self.sub_dir, self.file_name) self.afm.set_head_mode('contact') self.afm.clear_channels() self.afm.add_channel('ChannelZDriveOrTopography') self.afm.stop_scan() self.afm.set_z_scanner_range(self.parameter['z_scanner_range']) self.afm.set_xy_scanner_range(self.parameter['xy_scanner_range']) self.bck_scan_geometry = self.afm.get_scan_geometry_all() self.bck_scan_option = self.afm.get_scan_option_all() self.bck_approach_option = self.afm.get_approach_option_all() self.afm.set_scan_geometry(self.parameter['pixels'][0],self.parameter['pixels'][1], 0, 0) self.afm.set_scan_option(sine_scan=False,over_scan=False, \ over_scan_percent=0, tow_way=False, \ det_driven=False, force_slope_correction=False, \ interlace=False,slow_scan='off') self.afm.set_scan_rate(self.parameter['scan_rate']) self.afm.enable_z_servo() self.afm.set_z_servo(self.parameter['gain'], self.parameter['setpoint']) self.afm.set_approach_option() def unset(self): self.afm.set_scan_option_dict(self.bck_scan_option) self.afm.set_scan_geometry_dict(self.bck_scan_geometry) self.afm.set_scan_option_dict(self.bck_scan_option) def __approach(self): self.afm.start_approach('q+s') wait_time = self.parameter['post_approach_wait'] print(f"Waiting for {wait_time} seconds") sleep(wait_time) def __run(self): self.afm.trigger_image_scan() isscan = True while isscan: test = self.afm.query_scan_status() if test == 'true':isscan=True; else:isscan=False sleep(2) def approach(self): pass def run(self): pos = np.array(self.parameter['target_positions']) # 'target_positions'에서 좌표 데이터 로드 pos_int = [] # 문자열 배열을 정수형 배열로 변환(빈 문자열은 무시됨) for row in pos: if row[0] and row[1]: # 문자열이 비어 있지 않으면 try: pos_int.append([int(row[0]), int(row[1])]) except ValueError: print(f"Error converting position to integer: {row}") continue pos_int = np.array(pos_int) norm_speed_x = self.parameter['norm_speed'] norm_speed_y = self.parameter['norm_speed'] for i, (x, y) in enumerate(pos_int): # 반복문을 통해 좌표 이동 후 Zero Scan 수행 self.afm.moveto_xy_stage(x, y, norm_speed_x, norm_speed_y) reply = self.afm.query_stage_pos() sw_x = reply['x'] sw_y = reply['y'] sleep(2) # 로그 기록 line = f'{self.iter}, P{i}, {x}, {y}, {sw_x}, {sw_y}\n' self.file_name = f'RAW_Repeat_{self.iter}_P{i}' self.afm.set_data_location(self.base_dir, self.sub_dir, self.file_name) with open(self.log_path, "a") as log_fd: log_fd.write(line) self.__approach() self.__run() self.afm.lift_z_stage(1000) def done(self): self.afm.stop_scan() self.afm.stop_scan() if self.parameter['lift_enable'] == 'On': self.afm.lift_z_stage(1000) def stop(self): checker = CHECKER() checker.abort_approach() checker.abort_scan() def analyze(self): find_dir = self.base_dir + '\\' + self.sub_dir tiff_list = src.core.report.ls_tiff(find_dir) read_tiff = src.core.tiff.Read_tiff() write_tiff = src.core.tiff.Write_tiff() result_mean = [] result_min = [] result_max = [] result_name = [] result_rq = [] for tiff_path in tiff_list: tiff_name = tiff_path.split('\\')[-1] tmp_name = tiff_name.split('.')[0] condition_1 = tmp_name.split('_')[-3] == 'Z Height' condition_2 = tmp_name.split('_')[-2] == 'Forward' tmp_name_list = tiff_name.split('_') tmp_name = '' for s in tmp_name_list[1:]: tmp_name += '_' + s tmp_name = tmp_name.split('.')[0] tmp_name = tmp_name[1:] if condition_1 & condition_2: dict_tiff , ori_tiff = read_tiff(tiff_path) image = dict_tiff['IMAGE'] flatten_image = src.core.analysis.one_d_flatten(image) dict_tiff['IMAGE'] = flatten_image cmap = dict_tiff['COLORMAP'] save_path = self.base_dir + '\\' +self.sub_dir + '\\Flatten\\' + 'Flatten_' + tmp_name + '.tiff' report_dir = self.base_dir + '\\' +self.sub_dir + '\\Report\\' write_tiff(ori_tiff,dict_tiff,save_path) # convert scale : um ->nm scale_factor = 1E+3 tmp_mean, tmp_min, tmp_max = src.core.analysis.cal_statistic(flatten_image) tmp_mean *= scale_factor; tmp_min *= scale_factor; tmp_max *= scale_factor tmp_rq = src.core.analysis.cal_r_q(flatten_image) * scale_factor tmp_data = [ ['Mean', 'nm',f'{tmp_mean:.3f}'], ['Min', 'nm', f'{tmp_min:.3f}'], ['Max', 'nm', f'{tmp_max:.3f}'], ['Rq', 'nm', f'{tmp_rq:.3f}'] ] flatten_image *= scale_factor src.core.report.default_report(tmp_name, report_dir, dict_tiff, cmap, tmp_data) result_name.append(tmp_name) result_mean.append(tmp_mean) result_min.append(tmp_min) result_max.append(tmp_max) result_rq.append(tmp_rq) csv_path = self.base_dir + '\\' + self.sub_dir + "\\result.csv" result_dict = {'File name': result_name, 'Mean (nm)': result_mean, 'Min (nm)' : result_min, 'Max (nm)' : result_max, 'Rq (nm)' : result_rq } src.core.report.write_csv(result_dict,csv_path,['Mean (nm)','Min (nm)','Max (nm)','Rq (nm)'])